From cf64d604817a08231c5588c8f9a1ca1b1e4f26fa Mon Sep 17 00:00:00 2001 From: Tatiana Gurbich Date: Thu, 3 Aug 2023 15:10:07 +0100 Subject: [PATCH 1/4] Added GSC workshop files --- .../GSC23 MGnify Workshop.ipynb | 5534 +++++++++++++++++ .../all_genome_resources_Aug2023.parquet | Bin 0 -> 1488326 bytes 2 files changed, 5534 insertions(+) create mode 100644 src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb create mode 100644 src/notebooks/example-data/genomes/all_genome_resources_Aug2023.parquet diff --git a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb new file mode 100644 index 0000000..eaeb758 --- /dev/null +++ b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb @@ -0,0 +1,5534 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "6f011bcd-c994-4681-8863-65adc0430908", + "metadata": { + "tags": [] + }, + "source": [ + "---\n", + "title: \"GSC23 MGnify Workshop\"\n", + "authors: \"Tatiana Gurbich\", \"Sandy Rogers\", \"Virginie Grosboillot\"\n", + "categories: [Python]\n", + "execute: \n", + " enabled: true\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ce2d1dc9-1a56-4103-8353-5a5b3fdb24bc", + "metadata": { + "tags": [] + }, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5e7134e1-079b-421a-b634-261ce028e631", + "metadata": {}, + "source": [ + "\n", + "\n", + "# GSC23 MGnify Workshop Advanced Session - Practical Exercise\n", + "\n", + "### Aims:\n", + "\n", + "* become familiar with the MGnify API and learn to access it programmatically\n", + "* explore the MGnify Genomes resource using Python\n", + "\n", + "### How this notebook works: \n", + "\n", + "This is an interactive code notebook (a Jupyter Notebook).\n", + "To run this code, click into each cell and press the ▶ button in the top toolbar, or press `shift+enter`.\n", + "\n", + "### This notebook is divided into 3 sections: \n", + "\n", + "- Section 1 focuses on MGnify Analyses (metagenomic datasets and assemblies analysed by MGnify)\n", + "- Section 2 focuses on the MGnify Genomes resource (the genome catalogues)\n", + "- Bonus section allows you to practice writing your own code to query the API\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d35fcccb-bf90-4cdd-8c36-415e9b843469", + "metadata": {}, + "source": [ + "### Import packages\n", + "\n", + "Execute the code below to import the Python libraries required to run the examples presented in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 354, + "id": "ba4e90f6-7778-4d03-ae4f-05711d5d5c63", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Connection to MGnify API\n", + "from jsonapi_client import Session as APISession\n", + "from jsonapi_client import Modifier # allows us to query specific values in given fields (e.g.: 'taxon-lineage').\n", + "import requests\n", + "from itertools import islice\n", + "\n", + "# Dataframes and display\n", + "import pandas as pd\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# Plots\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import plotly.graph_objects as go\n", + "%matplotlib inline \n", + "\n", + "# Data download\n", + "from urllib.request import urlretrieve\n", + "\n", + "# Warning verbosity\n", + "import warnings \n", + "warnings.filterwarnings(action=\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "16229ed2-70b0-4e5a-a2ec-3f8d63a227e9", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Section 1: explore MGnify analysis results programmatically" + ] + }, + { + "cell_type": "markdown", + "id": "0310546e-6b7a-41c9-a42a-7a03f0a16ab0", + "metadata": {}, + "source": [ + "In the first portion of today's workhop you learned about MGnify's analysis of metagenomic data. In this exercise we will fetch and explore MGnify analysis results programmatically. These are just a few examples of the types of commands and operations that can be peformed." + ] + }, + { + "cell_type": "markdown", + "id": "ce46df50-8a47-40d0-a0aa-499fa333b4ac", + "metadata": {}, + "source": [ + "#### Task 1: Browse the recently analysed studies and save them to a file\n", + "\n", + "Let's start by loading 50 most recently analysed studies and saving them to a file called ```studies.csv``` (it would take too long to load all studies for this example so we limit the number at 50):" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "80005880-ec79-4991-96b0-b1d8848eac99", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "endpoint = 'studies'\n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " resources = map(lambda r: r.json, islice(mgnify.iterate(endpoint), 50))\n", + " resources = pd.json_normalize(resources)\n", + " resources.to_csv(f\"{endpoint}.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "eadfa1bd-fc42-47a5-b760-7bc573d159da", + "metadata": {}, + "source": [ + "Click on the three dots below to open up a line by line explanation of this code block." + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "id": "0941782b-69e3-4c48-b036-fca19d9dde40", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "endpoint = 'studies'\n", + "# An \"endpoint\" is the specific resource within the API which we want to get data from. \n", + "# It is the URL relative to the \"server base URL\" of the API, which for MGnify is \n", + "# https://www.ebi.ac.uk/metagenomics/api/v1.\n", + "# You can find the endpoints in the API Docs https://www.ebi.ac.uk/metagenomics/api/docs/ \n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " # Calling \"APISession\" is enabling a connection to the MGnify API, that can be used multiple times. \n", + " # The `with...as mgnify` syntax is a Python \"context\". \n", + " # Everything inside the `with...` block (i.e. indented lines below it) can use the APISession \n", + " # which we've called mgnify here. \n", + " # When the `with` block closes (the indentation stops), the connection to the API is cleaned up.\n", + "\n", + " resources = map(lambda r: r.json, islice(mgnify.iterate(endpoint), 50))\n", + " # `map` applies a function to every element of an iterable - so do something to each thing in a list.\n", + " # The MGnify API is paginated, which means query results are broken up into pages for easier handling.\n", + " # `mgnify.iterate(endpoint)` is a very helpful function that loops over as many pages of results as \n", + " # there are.\n", + " # `lambda r: r.json` is grabbing the JSON attribute from each study returned from the API.\n", + " # The islice() function from the itertools module is used to limit the number of results to 50. \n", + " # It ensures that only the first 50 items will be processed. We added this to avoid loading all of \n", + " # the studies, which would take a long time.\n", + " # All together, this makes `resources` be 50 JSON representations we could loop through, each containing \n", + " # the data of a study.\n", + "\n", + " resources = pd.json_normalize(resources)\n", + " # pd is the shorthand for the pandas package - you'll see it anywhere people are using pandas.\n", + " # The json_normalize function takes \"nested\" data and turns it into a table.\n", + "\n", + " resources.to_csv(f\"{endpoint}.csv\")\n", + " # Pandas has a built-in way of writing CSV (or TSV, etc) files, which is helpful for getting data into \n", + " # other tools. This writes the table-ified study list to a file called studies.csv." + ] + }, + { + "cell_type": "markdown", + "id": "2279b659-f142-4000-99d3-510484465039", + "metadata": { + "tags": [] + }, + "source": [ + "Studies have been saved to file. Double-click on the file name ```studies.csv``` in the left-hand panel to open it. You can see the list of study accessions and information about each of them." + ] + }, + { + "cell_type": "markdown", + "id": "a6ce33e3-0176-469b-834d-1018ed3c291c", + "metadata": {}, + "source": [ + "#### Task 2: Explore a selected a study\n", + "\n", + "Pick a study to load and explore in more detail. In the code below we picked study MGYS00001935 as the default, however, you can choose a different study accession from [the MGnify website](https://www.ebi.ac.uk/metagenomics/browse/studies/) or from the ```studies.csv``` file you just generated. When you execute the code block below, a prompt window will appear. When that happens, paste the study accession you chose into the window and press 'Enter'. To use the default, press 'Enter' without pasting anything into the window. " + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "id": "d1661765-bdf8-44b5-9c10-db0ffe7fb4d5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Type a Study Accession [default: MGYS00001935] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using \"MGYS00001935\" as Study Accession\n" + ] + } + ], + "source": [ + "from lib.variable_utils import get_variable_from_link_or_input\n", + "\n", + "accession = get_variable_from_link_or_input('MGYS', 'Study Accession', 'MGYS00001935')\n", + "\n", + "# you could also assign the accession value directly:\n", + "# accession = 'MGYS00001935'" + ] + }, + { + "cell_type": "markdown", + "id": "cb54b909-6dc4-4982-bd11-f03ca830d982", + "metadata": {}, + "source": [ + "#### Fetch data for the selected study\n", + "\n", + "Load analyses from this study into a [Pandas dataframe](https://pandas.pydata.org/docs/user_guide/index.html). Pandas is a Python library that is widely used to work with and manipulate data. A dataframe is a table with rows and columns. Note that we will be using a different endpoint here because now we are fetching analyses rather than studies." + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "id": "5173ced5-e607-49a2-88d1-88c57d68baaf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "endpoint = f\"studies/{accession}/analyses\"\n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " analyses = map(lambda r: r.json, mgnify.iterate(endpoint))\n", + " analyses = pd.json_normalize(analyses)" + ] + }, + { + "cell_type": "markdown", + "id": "8992df39-d616-464c-a2dd-bb7f5d0cb352", + "metadata": {}, + "source": [ + "#### Inspect the data\n", + "\n", + "Analyses from the study are now loaded into the dataframe ```analyses```. The ```.head()``` method prints the first few rows of the table:" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "id": "f1071967-42f7-4890-99dd-58d138ebffac", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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nucleotide sequences', 'value': '9967'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after length filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '9113'}, {'key': 'Predicted SSU sequences', 'value': '8431'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "2 [{'key': 'Submitted nucleotide sequences', 'value': '8658'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7850'}, {'key': 'Predicted SSU sequences', 'value': '7057'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "3 [{'key': 'Submitted nucleotide sequences', 'value': '7008'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after length filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '6632'}, {'key': 'Predicted SSU sequences', 'value': '6556'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "4 [{'key': 'Submitted nucleotide sequences', 'value': '8317'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7225'}, {'key': 'Predicted SSU sequences', 'value': '6692'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "\n", + " attributes.experiment-type attributes.is-private attributes.complete-time \\\n", + "0 amplicon False 2023-07-19T14:11:39 \n", + "1 amplicon False 2023-07-19T14:11:59 \n", + "2 amplicon False 2023-07-19T14:12:24 \n", + "3 amplicon False 2023-07-19T14:12:50 \n", + "4 amplicon False 2023-07-19T14:13:10 \n", + "\n", + " attributes.instrument-platform attributes.instrument-model \\\n", + "0 LS454 454 GS FLX \n", + "1 LS454 454 GS FLX \n", + "2 LS454 454 GS FLX \n", + "3 LS454 454 GS FLX \n", + "4 LS454 454 GS FLX \n", + "\n", + " relationships.sample.data.id relationships.sample.data.type \\\n", + "0 ERS161286 samples \n", + "1 ERS161300 samples \n", + "2 ERS161297 samples \n", + "3 ERS161295 samples \n", + "4 ERS161294 samples \n", + "\n", + " relationships.run.data.id relationships.run.data.type \\\n", + "0 ERR162887 runs \n", + "1 ERR162901 runs \n", + "2 ERR162898 runs \n", + "3 ERR162896 runs \n", + "4 ERR162895 runs \n", + "\n", + " relationships.study.data.id relationships.study.data.type \n", + "0 MGYS00006253 studies \n", + "1 MGYS00006253 studies \n", + "2 MGYS00006253 studies \n", + "3 MGYS00006253 studies \n", + "4 MGYS00006253 studies " + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyses.head()" + ] + }, + { + "cell_type": "markdown", + "id": "5fc2218f-0330-4e41-92f4-7c619fdcf8b2", + "metadata": {}, + "source": [ + "#### Exploring the data - Example 1: distribution of sequencing instruments used for the analysed samples" + ] + }, + { + "cell_type": "markdown", + "id": "c735f165-ae58-4e0c-a1fd-312f2701c6fd", + "metadata": {}, + "source": [ + "Let's make a plot showing what instrument type(s) were used to sequence the samples in the study we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "id": "fd8ef42e-ea80-4a55-863e-12342dddab5a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "analyses.groupby('attributes.instrument-model').size().plot(kind='pie', autopct='%1.1f%%')\n", + "plt.title('Percentages of analysed samples by instrument type');" + ] + }, + { + "cell_type": "markdown", + "id": "ce30b73c-90f6-4bfb-b0ef-5245f6f54d58", + "metadata": {}, + "source": [ + "Click on the three dots below to open up an explanation of how this code works." + ] + }, + { + "cell_type": "markdown", + "id": "5df38bd1-428e-47c9-adf2-8478ca15cf03", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "```analyses.groupby('attributes.instrument-model')```: the groupby operation here groups the data \n", + "in the dataframe ```analyses``` based on the values in the column ```attributes.instrument-model```. That means it groups the data according to the instrument models mentioned in that column.\n", + "\n", + "The ```.size()``` function is applied after grouping to count the number of occurrences (size) of each group. It counts how many samples were analyzed using each instrument model.\n", + "\n", + "```.plot(kind='pie', autopct='%1.1f%%')```: this line creates a pie chart from the grouped and counted data. The ```kind='pie'``` parameter specifies that a pie chart should be plotted. The ```autopct='%1.1f%%'``` parameter formats the percentage values in the pie chart to show one decimal place.\n", + "\n", + "```plt.title('Percentages of analysed samples by instrument type')```: this line sets the title of the pie chart to \"Percentages of analysed samples by instrument type.\"" + ] + }, + { + "cell_type": "markdown", + "id": "7ff11f02-b375-4d15-bcf0-96dcca43c99b", + "metadata": { + "tags": [] + }, + "source": [ + "#### Exploring the data - Example 2: filtering results\n", + "\n", + "You might have noticed while exploring the dataframe ```analyses``` above that the column ```attributes.analysis-summary``` contains JSONs as its values. Let's run a Python code to add the information from these JSONs as new columns to the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "id": "0fee9320-4354-47ce-afbc-e44b2ef09c50", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.experiment-typeattributes.accessionattributes.analysis-statusattributes.pipeline-versionattributes.is-privateattributes.complete-timeattributes.instrument-platformattributes.instrument-modelrelationships.study.data.idrelationships.study.data.typerelationships.run.data.idrelationships.run.data.typerelationships.sample.data.idrelationships.sample.data.typeSubmitted nucleotide sequencesNucleotide sequences after format-specific filteringNucleotide sequences after length filteringNucleotide sequences after undetermined bases filteringPredicted SSU sequencesPredicted LSU sequencesReads with predicted CDSReads with predicted RNAReads with InterProScan matchPredicted CDSPredicted CDS with InterProScan matchTotal InterProScan matches
0analysis-jobsMGYA00646965ampliconMGYA00646965completed5.0False2023-07-19T14:11:39LS454454 GS FLXMGYS00006253studiesERR162887runsERS161286samples16279115151151511515100460N/AN/AN/AN/AN/AN/A
1analysis-jobsMGYA00646967ampliconMGYA00646967completed5.0False2023-07-19T14:11:59LS454454 GS FLXMGYS00006253studiesERR162901runsERS161300samples996791139113911384310N/AN/AN/AN/AN/AN/A
2analysis-jobsMGYA00646970ampliconMGYA00646970completed5.0False2023-07-19T14:12:24LS454454 GS FLXMGYS00006253studiesERR162898runsERS161297samples865878507850785070570N/AN/AN/AN/AN/AN/A
3analysis-jobsMGYA00646973ampliconMGYA00646973completed5.0False2023-07-19T14:12:50LS454454 GS FLXMGYS00006253studiesERR162896runsERS161295samples700866326632663265560N/AN/AN/AN/AN/AN/A
4analysis-jobsMGYA00646976ampliconMGYA00646976completed5.0False2023-07-19T14:13:10LS454454 GS FLXMGYS00006253studiesERR162895runsERS161294samples831772257225722566920N/AN/AN/AN/AN/AN/A
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" + ], + "text/plain": [ + " type id attributes.experiment-type \\\n", + "0 analysis-jobs MGYA00646965 amplicon \n", + "1 analysis-jobs MGYA00646967 amplicon \n", + "2 analysis-jobs MGYA00646970 amplicon \n", + "3 analysis-jobs MGYA00646973 amplicon \n", + "4 analysis-jobs MGYA00646976 amplicon \n", + "\n", + " attributes.accession attributes.analysis-status attributes.pipeline-version \\\n", + "0 MGYA00646965 completed 5.0 \n", + "1 MGYA00646967 completed 5.0 \n", + "2 MGYA00646970 completed 5.0 \n", + "3 MGYA00646973 completed 5.0 \n", + "4 MGYA00646976 completed 5.0 \n", + "\n", + " attributes.is-private attributes.complete-time \\\n", + "0 False 2023-07-19T14:11:39 \n", + "1 False 2023-07-19T14:11:59 \n", + "2 False 2023-07-19T14:12:24 \n", + "3 False 2023-07-19T14:12:50 \n", + "4 False 2023-07-19T14:13:10 \n", + "\n", + " attributes.instrument-platform attributes.instrument-model \\\n", + "0 LS454 454 GS FLX \n", + "1 LS454 454 GS FLX \n", + "2 LS454 454 GS FLX \n", + "3 LS454 454 GS FLX \n", + "4 LS454 454 GS FLX \n", + "\n", + " relationships.study.data.id relationships.study.data.type \\\n", + "0 MGYS00006253 studies \n", + "1 MGYS00006253 studies \n", + "2 MGYS00006253 studies \n", + "3 MGYS00006253 studies \n", + "4 MGYS00006253 studies \n", + "\n", + " relationships.run.data.id relationships.run.data.type \\\n", + "0 ERR162887 runs \n", + "1 ERR162901 runs \n", + "2 ERR162898 runs \n", + "3 ERR162896 runs \n", + "4 ERR162895 runs \n", + "\n", + " relationships.sample.data.id relationships.sample.data.type \\\n", + "0 ERS161286 samples \n", + "1 ERS161300 samples \n", + "2 ERS161297 samples \n", + "3 ERS161295 samples \n", + "4 ERS161294 samples \n", + "\n", + " Submitted nucleotide sequences \\\n", + "0 16279 \n", + "1 9967 \n", + "2 8658 \n", + "3 7008 \n", + "4 8317 \n", + "\n", + " Nucleotide sequences after format-specific filtering \\\n", + "0 11515 \n", + "1 9113 \n", + "2 7850 \n", + "3 6632 \n", + "4 7225 \n", + "\n", + " Nucleotide sequences after length filtering \\\n", + "0 11515 \n", + "1 9113 \n", + "2 7850 \n", + "3 6632 \n", + "4 7225 \n", + "\n", + " Nucleotide sequences after undetermined bases filtering \\\n", + "0 11515 \n", + "1 9113 \n", + "2 7850 \n", + "3 6632 \n", + "4 7225 \n", + "\n", + " Predicted SSU sequences Predicted LSU sequences Reads with predicted CDS \\\n", + "0 10046 0 N/A \n", + "1 8431 0 N/A \n", + "2 7057 0 N/A \n", + "3 6556 0 N/A \n", + "4 6692 0 N/A \n", + "\n", + " Reads with predicted RNA Reads with InterProScan match Predicted CDS \\\n", + "0 N/A N/A N/A \n", + "1 N/A N/A N/A \n", + "2 N/A N/A N/A \n", + "3 N/A N/A N/A \n", + "4 N/A N/A N/A \n", + "\n", + " Predicted CDS with InterProScan match Total InterProScan matches \n", + "0 N/A N/A \n", + "1 N/A N/A \n", + "2 N/A N/A \n", + "3 N/A N/A \n", + "4 N/A N/A " + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Save all possible JSON keys into a list \"all_keys\"\n", + "all_keys = [\n", + " 'Submitted nucleotide sequences',\n", + " 'Nucleotide sequences after format-specific filtering',\n", + " 'Nucleotide sequences after length filtering',\n", + " 'Nucleotide sequences after undetermined bases filtering',\n", + " 'Predicted SSU sequences',\n", + " 'Predicted LSU sequences',\n", + " 'Reads with predicted CDS',\n", + " 'Reads with predicted RNA',\n", + " 'Reads with InterProScan match',\n", + " 'Predicted CDS',\n", + " 'Predicted CDS with InterProScan match',\n", + " 'Total InterProScan matches'\n", + "]\n", + "\n", + "# Iterate over each row of the 'attributes.analysis-summary' column, extract information \n", + "# and save to a temporary dictionary. If an analysis doesn't have a value for any of the keys\n", + "# from the all_keys list, insert \"N/A\"\n", + "extracted_data = dict()\n", + "for row in analyses['attributes.analysis-summary']:\n", + " result_dict = {item['key']: item['value'] for item in row}\n", + " for key in all_keys:\n", + " add_value = result_dict[key] if key in result_dict else \"N/A\"\n", + " extracted_data.setdefault(key, list()).append(add_value)\n", + " \n", + "# Convert the dictionary to a DataFrame\n", + "extracted_df = pd.DataFrame(extracted_data)\n", + "\n", + "# Concatenate the two dataframes into a new one called transformed_analyses \n", + "transformed_analyses = pd.concat([analyses, extracted_df], axis=1)\n", + "\n", + "# Remove the attributes.analysis-summary column from the new dataframe\n", + "transformed_analyses.drop('attributes.analysis-summary', axis=1, inplace=True)\n", + "\n", + "# Display the first few rows of the resulting dataframe\n", + "transformed_analyses.head()" + ] + }, + { + "cell_type": "markdown", + "id": "99450b02-263d-4ed0-bb1b-4987dde678b6", + "metadata": {}, + "source": [ + "Scroll to the right to see the newly added fields.\n", + "\n", + "Let's filter the results to only show analyses that have at least 1 predicted LSU (large ribosomal subunit) sequence:" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "id": "e1a219d6-26e5-4e7e-8c6c-571cb32ea9d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.accessionattributes.analysis-statusattributes.pipeline-versionattributes.experiment-typeattributes.is-privateattributes.complete-timeattributes.instrument-platformattributes.instrument-modelrelationships.sample.data.idrelationships.sample.data.typerelationships.run.data.idrelationships.run.data.typerelationships.study.data.idrelationships.study.data.typeSubmitted nucleotide sequencesNucleotide sequences after format-specific filteringNucleotide sequences after length filteringNucleotide sequences after undetermined bases filteringPredicted SSU sequencesPredicted LSU sequencesReads with predicted CDSReads with predicted RNAReads with InterProScan matchPredicted CDSPredicted CDS with InterProScan matchTotal InterProScan matches
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [type, id, attributes.accession, attributes.analysis-status, attributes.pipeline-version, attributes.experiment-type, attributes.is-private, attributes.complete-time, attributes.instrument-platform, attributes.instrument-model, relationships.sample.data.id, relationships.sample.data.type, relationships.run.data.id, relationships.run.data.type, relationships.study.data.id, relationships.study.data.type, Submitted nucleotide sequences, Nucleotide sequences after format-specific filtering, Nucleotide sequences after length filtering, Nucleotide sequences after undetermined bases filtering, Predicted SSU sequences, Predicted LSU sequences, Reads with predicted CDS, Reads with predicted RNA, Reads with InterProScan match, Predicted CDS, Predicted CDS with InterProScan match, Total InterProScan matches]\n", + "Index: []" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a new dataframe called filtered_analyses which will include all lines from the\n", + "# transformed_analyses dataframe except for the ones where the value in the \"Predicted LSU sequences\"\n", + "# column is \"0\" or \"N/A\"\n", + "filtered_analyses = transformed_analyses[\n", + " ~(transformed_analyses['Predicted LSU sequences'].isin(['0', 'N/A']))\n", + "]\n", + "\n", + "# print the filtered dataframe\n", + "filtered_analyses" + ] + }, + { + "cell_type": "markdown", + "id": "fefb31c2-0b37-4f26-b8e1-9a6649dbb96c", + "metadata": { + "tags": [] + }, + "source": [ + "##### Q: How many analyses have at least 1 predicted LSU? \n", + "\n", + "Hint: look at the dataframe dimensions that are printed below the dataframe. What is the number of rows? Note that some studies might not have any predicted LSU sequences and the dataframe above could be empty. \n", + "You could also find the number of rows in the dataframe by executing the code:" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "id": "6e0210f7-d788-48a7-b327-2763b8183bbb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_analyses.shape[0]" + ] + }, + { + "cell_type": "markdown", + "id": "c8dc54b1-7dfe-4b23-8b5e-872fd54d10f0", + "metadata": {}, + "source": [ + "You can similarly filter the results using any of the other columns depending on what you are interested in." + ] + }, + { + "cell_type": "markdown", + "id": "b38075b5-8348-4730-bdd6-2fbd4a8a8e10", + "metadata": {}, + "source": [ + "#### Exploring the data - Example 3: download the analysis results files" + ] + }, + { + "cell_type": "markdown", + "id": "814024f7-a5be-4974-b183-62ddd6360f00", + "metadata": { + "tags": [] + }, + "source": [ + "Now let's fetch the actual results files. To do that we will use the ```/analyses/{accession}/downloads``` endpoint. For the purposes of this exercise we will download the results for only the first 2 analyses from the unfiltered dataframe ```analyses``` but in your own work you could download them all or download only the ones that passed the filters that you applied. \n", + "\n", + "There are different result files available, let's first check which ones we could download:" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "id": "269794af-c4c8-49c3-a832-8c0ce8a5241b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Files available for analysis MGYA00646965:\n", + "ERR162901_FASTQ.fasta.gz: Processed nucleotide reads\n", + "ERR162901_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", + "ERR162901_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", + "ERR162901_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR162901_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR162901_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "\n", + "Files available for analysis MGYA00646967:\n", + "ERR162901_FASTQ.fasta.gz: Processed nucleotide reads\n", + "ERR162901_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", + "ERR162901_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", + "ERR162901_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR162901_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR162901_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n" + ] + } + ], + "source": [ + "endpoint = f\"analyses/{analysisId}/downloads\"\n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session:\n", + " for analysisId in analyses.head(2)['attributes.accession']:\n", + " print(f\"\\nFiles available for analysis {analysisId}:\")\n", + " for download in session.iterate(endpoint):\n", + " print(f\"{download.alias}: {download.description.label}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a697378b-a026-4748-8d0e-607835919b0a", + "metadata": {}, + "source": [ + "As an example, let's download only the tables containing the taxonomic assignments for SSU rRNA." + ] + }, + { + "cell_type": "code", + "execution_count": 373, + "id": "aef7bd3e-79d5-44cf-ba47-a8e6259c21d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: MGYA00646965\n", + "Downloading file for MGYA00646965\n", + "Finished for MGYA00646965\n", + "Processing: MGYA00646967\n", + "Downloading file for MGYA00646967\n", + "Finished for MGYA00646967\n" + ] + } + ], + "source": [ + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session: \n", + " for analysisId in analyses.head(2)['attributes.accession']:\n", + " print(f\"Processing: {analysisId}\")\n", + " for download in session.iterate(\n", + " f\"analyses/{analysisId}/downloads\"\n", + " ):\n", + " # Start another for loop to go over the files to download\n", + " OTUFiles = f\"{analysisId}_MERGED_FASTQ_SSU_OTU.tsv\"\n", + " if (\n", + " download.description.label =='OTUs, counts and taxonomic assignments for SSU rRNA'\n", + " and\n", + " download.file_format.name == 'TSV'\n", + " ):\n", + " print(f\"Downloading file for {analysisId}\")\n", + " urlretrieve(download.links.self.url, OTUFiles)\n", + " break\n", + " print(\"Finished for\", analysisId)" + ] + }, + { + "cell_type": "markdown", + "id": "b0bc7f21-ce82-4fcf-9778-f96e441c2b9e", + "metadata": {}, + "source": [ + "Click on the three dots below to see an explanation for how this code works." + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "id": "8d40a77f-fd58-4d9b-a5f9-95edbc49dcbe", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: MGYA00646965\n", + "Downloading file for MGYA00646965\n", + "Finished for MGYA00646965\n", + "Processing: MGYA00646967\n", + "Downloading file for MGYA00646967\n", + "Finished for MGYA00646967\n" + ] + } + ], + "source": [ + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session:\n", + " # Starting a connection to MGnify's API just like we did before\n", + " \n", + " for analysisId in analyses.head(2)['attributes.accession']:\n", + " # Here we are starting a for loop to iterate over values in the 'attributes.accession' column\n", + " # of the analyses dataframe (this column contains analysis accessions). However, we only want \n", + " # to iterate over the first two accessions which is why we use the .head(2) method to give us \n", + " # just the first 2 records.\n", + " \n", + " print(f\"Processing: {analysisId}\")\n", + " # Print the analysis accession we are currently processing\n", + " \n", + " for download in session.iterate(\n", + " f\"analyses/{analysisId}/downloads\"\n", + " ):\n", + " # Start another for loop and use the .iterate() method to retrieve information about downloads \n", + " # associated with the current analysis ID. The method call is formatted with the analysisId to \n", + " # form the specific API endpoint: analyses/{analysisId}/downloads where the value for analysisId\n", + " # will be plugged in for each iteration.\n", + " \n", + " OTUFiles = f\"{analysisId}_MERGED_FASTQ_SSU_OTU.tsv\"\n", + " # This line generates the file name of the OTU results file: analysis accession followed by the\n", + " # string \"_MERGED_FASTQ_SSU_OTU.tsv\"\n", + " \n", + " if (\n", + " download.description.label =='OTUs, counts and taxonomic assignments for SSU rRNA'\n", + " and\n", + " download.file_format.name == 'TSV'\n", + " ):\n", + " # We only want to download the OTU files in this case and not any other result files. \n", + " # This piece of code is looking for those files by checking the format and file description.\n", + " # Once the correct format and description are found, the next block of code is executed.\n", + " print(f\"Downloading file for {analysisId}\")\n", + " urlretrieve(download.links.self.url, OTUFiles)\n", + " # Fetch the file link to which is stored in download.links.self.url. When saving the file locally,\n", + " # give it the name stored in the variable OTUFiles (f\"{analysisId}_MERGED_FASTQ_SSU_OTU.tsv\")\n", + " break\n", + " # After downloading the file, the code breaks out of the inner for loop since only one matching \n", + " # file is needed for each analysis ID. We don't need to check any more records.\n", + " print(\"Finished for\", analysisId)\n" + ] + }, + { + "cell_type": "markdown", + "id": "2dd2a1af-9373-4582-81c1-b50d067ea488", + "metadata": {}, + "source": [ + "You can see the downloaded tables in the left-hand panel. Double-click the file names to open and browse them." + ] + }, + { + "cell_type": "markdown", + "id": "6d56034b-3394-4753-ac02-71913ab5a691", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Section 2: exploring the MGnify Genomes resource programmatically" + ] + }, + { + "cell_type": "markdown", + "id": "d2ff1389-1e5e-492c-a22d-cfc4838cdcab", + "metadata": {}, + "source": [ + "In this section of the practical exercise we will look at a few ways how Python can be used to explore the MGnify Genomes resource via the API. " + ] + }, + { + "cell_type": "markdown", + "id": "a0657dbc-7463-4c0c-81f3-2d770e281002", + "metadata": { + "tags": [] + }, + "source": [ + "#### Task 1: Query the ```genomes``` database from MGnify API" + ] + }, + { + "cell_type": "markdown", + "id": "57850ce0-c9b3-49e6-a66e-dc1c4fd7dffa", + "metadata": {}, + "source": [ + "We will be using the ```genomes``` endpoint to work with MGnify Genomes:" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "id": "5277dccd-35b4-43b4-b53c-cd23ce655222", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "endpoint = 'genomes'" + ] + }, + { + "cell_type": "markdown", + "id": "6d4a3c41-97c7-4ff2-92d6-c3278844b409", + "metadata": {}, + "source": [ + "Let's fetch data from this endpoint and see what the first result looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "id": "967be7ab-ec02-4759-a09c-e383ab874769", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'genomes',\n", + " 'id': 'MGYG000321626',\n", + " 'attributes': {'genome-id': 17285,\n", + " 'geographic-range': [],\n", + " 'geographic-origin': 'Europe',\n", + " 'accession': 'MGYG000321626',\n", + " 'ena-genome-accession': None,\n", + " 'ena-sample-accession': 'SAMN33097420',\n", + " 'ena-study-accession': None,\n", + " 'ncbi-genome-accession': 'GCA_028655675',\n", + " 'ncbi-sample-accession': None,\n", + " 'ncbi-study-accession': 'PRJNA931735',\n", + " 'img-genome-accession': None,\n", + " 'patric-genome-accession': None,\n", + " 'length': 1881475,\n", + " 'num-contigs': 145,\n", + " 'n-50': 32562,\n", + " 'gc-content': 49.81,\n", + " 'type': 'Isolate',\n", + " 'completeness': 99.03,\n", + " 'contamination': 0.0,\n", + " 'rna-5s': 93.28,\n", + " 'rna-16s': 99.41,\n", + " 'rna-23s': 97.64,\n", + " 'trnas': 20.0,\n", + " 'nc-rnas': 26,\n", + " 'num-proteins': 1803,\n", + " 'eggnog-coverage': 95.73,\n", + " 'ipr-coverage': 89.24,\n", + " 'taxon-lineage': 'd__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii',\n", + " 'num-genomes-total': 1,\n", + " 'pangenome-size': None,\n", + " 'pangenome-core-size': None,\n", + " 'pangenome-accessory-size': None,\n", + " 'last-update': '2023-07-07T11:35:45.488929',\n", + " 'first-created': '2023-07-07T11:35:45.486483'},\n", + " 'relationships': {'antismash-geneclusters': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/antismash-genecluster'}},\n", + " 'kegg-class-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/kegg-class'}},\n", + " 'catalogue': {'data': {'type': 'genome-catalogues',\n", + " 'id': 'honeybee-gut-v1-0'},\n", + " 'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genome-catalogues/honeybee-gut-v1-0'}},\n", + " 'downloads': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/downloads'}},\n", + " 'cog-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/cogs'}},\n", + " 'kegg-modules-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/kegg-module'}},\n", + " 'biome': {'data': {'type': 'biomes',\n", + " 'id': 'root:Host-associated:Insecta:Digestive system'},\n", + " 'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/biomes/root:Host-associated:Insecta:Digestive%20system'}}},\n", + " 'links': {'self': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626'}}" + ] + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r = requests.get(f\"https://www.ebi.ac.uk/metagenomics/api/v1/{endpoint}\")\n", + "r.json()['data'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "29d32b22-ca8d-4a85-b44c-0c5a1a5e2fbd", + "metadata": {}, + "source": [ + "Here we are looking at one genome, the metadata for this genome, results summary and links. You can see what types of information we can get for each genome from this API endpoint. Take a moment to look over the output." + ] + }, + { + "cell_type": "markdown", + "id": "6b4a903a-beb9-4fe3-bde9-b280fe3472b1", + "metadata": {}, + "source": [ + "##### Q: can you spot what catalogue this genome is from?" + ] + }, + { + "cell_type": "markdown", + "id": "0202fa29-aca9-4d57-92b1-7148795443fc", + "metadata": {}, + "source": [ + "##### Q: what is the taxon lineage for this genome?" + ] + }, + { + "cell_type": "markdown", + "id": "119f301d-7b85-4744-9d38-d4908e706948", + "metadata": {}, + "source": [ + "#### Task 2: Search for a specific taxon" + ] + }, + { + "cell_type": "markdown", + "id": "17c03f3b-cba1-4f5e-ba2c-ff4041086f43", + "metadata": {}, + "source": [ + "If we are interested in a specific taxon, for example, a specific genus or species, we can find information about it from the API. \n", + "Let's use the genus Prevotella and the species Prevotella melaninogenica as our examples. \n", + "\n", + "The ```taxon-lineage``` field contains domain, phylum, class, order, family, genus, species in the following format: \n", + "```d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica```\n", + "\n", + "If we wanted to filter the database for the genus Prevotella we could use the full lineage: ```d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella``` \n", + "or only part of it: \n", + "```g__Prevotella``` or ```Prevotella```." + ] + }, + { + "cell_type": "markdown", + "id": "d3c8c76c-b89c-4a9e-a12f-af4b00193647", + "metadata": {}, + "source": [ + "##### Set the desired filters" + ] + }, + { + "cell_type": "code", + "execution_count": 356, + "id": "ae24320a-813b-4658-bb09-63313e8195bb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "genus_filter = 'Prevotella'\n", + "species_filter = 'Prevotella melaninogenica'" + ] + }, + { + "cell_type": "markdown", + "id": "af37a489-325f-4e9a-84a8-cfbf86839734", + "metadata": {}, + "source": [ + "##### Query the database with the species filter and save to a Pandas DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "id": "ddb1d36f-a3a4-4d25-85a7-3b68281ccdf4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " search_filter_sp = Modifier(f\"taxon_lineage={species_filter}\")\n", + " resources_sp = map(lambda r: r.json, mgnify.iterate(endpoint, filter=search_filter_sp))\n", + " resources_df_sp = pd.json_normalize(resources_sp)\n", + " \n", + "# Here we are using the Modifier function from the jsonapi_client module. It allows us \n", + "# to query for specific values in given fields (e.g.: 'taxon-lineage', 'geographic-origin').\n", + "# In this case we are adding a filter that would limit results to those that have the species\n", + "# name that we have saved in the species_filter variable (Prevotella melaninogenica) in the \n", + "# taxon_lineage field." + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "id": "16da3b82-4bd3-4043-b854-6da4bf39c149", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ena-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.last-updateattributes.first-createdrelationships.catalogue.data.idrelationships.catalogue.data.typerelationships.biome.data.idrelationships.biome.data.type
0genomesMGYG00029920713928Europe[North America, Europe]MGYG000299207ERS7621657ERP12511730444751015361340.71MAG99.320.060.00.00.019.020240588.8184.12d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica37504578142642021-12-17T10:56:55.4213212021-12-15T12:21:00.266046human-oral-v1-0genome-cataloguesroot:Host-associated:Human:Digestive system:Oralbiomes
1genomesMGYG0000032877834North America[North America, Europe, Asia]MGYG000003287SRS147088SRP00216332592431455718940.52MAG96.850.340.00.00.019.023272187.0379.64d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica4252064318772021-12-07T19:41:43.4562272021-12-07T19:41:43.453627human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomes
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" + ], + "text/plain": [ + " type id attributes.genome-id attributes.geographic-origin \\\n", + "0 genomes MGYG000299207 13928 Europe \n", + "1 genomes MGYG000003287 7834 North America \n", + "\n", + " attributes.geographic-range attributes.accession \\\n", + "0 [North America, Europe] MGYG000299207 \n", + "1 [North America, Europe, Asia] MGYG000003287 \n", + "\n", + " attributes.ena-sample-accession attributes.ena-study-accession \\\n", + "0 ERS7621657 ERP125117 \n", + "1 SRS147088 SRP002163 \n", + "\n", + " attributes.length attributes.num-contigs attributes.n-50 \\\n", + "0 3044475 101 53613 \n", + "1 3259243 145 57189 \n", + "\n", + " attributes.gc-content attributes.type attributes.completeness \\\n", + "0 40.71 MAG 99.32 \n", + "1 40.52 MAG 96.85 \n", + "\n", + " attributes.contamination attributes.rna-5s attributes.rna-16s \\\n", + "0 0.06 0.0 0.0 \n", + "1 0.34 0.0 0.0 \n", + "\n", + " attributes.rna-23s attributes.trnas attributes.nc-rnas \\\n", + "0 0.0 19.0 20 \n", + "1 0.0 19.0 23 \n", + "\n", + " attributes.num-proteins attributes.eggnog-coverage \\\n", + "0 2405 88.81 \n", + "1 2721 87.03 \n", + "\n", + " attributes.ipr-coverage \\\n", + "0 84.12 \n", + "1 79.64 \n", + "\n", + " attributes.taxon-lineage \\\n", + "0 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica \n", + "1 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica \n", + "\n", + " attributes.num-genomes-total attributes.pangenome-size \\\n", + "0 37 5045 \n", + "1 4 2520 \n", + "\n", + " attributes.pangenome-core-size attributes.pangenome-accessory-size \\\n", + "0 781 4264 \n", + "1 643 1877 \n", + "\n", + " attributes.last-update attributes.first-created \\\n", + "0 2021-12-17T10:56:55.421321 2021-12-15T12:21:00.266046 \n", + "1 2021-12-07T19:41:43.456227 2021-12-07T19:41:43.453627 \n", + "\n", + " relationships.catalogue.data.id relationships.catalogue.data.type \\\n", + "0 human-oral-v1-0 genome-catalogues \n", + "1 human-gut-v2-0-1 genome-catalogues \n", + "\n", + " relationships.biome.data.id \\\n", + "0 root:Host-associated:Human:Digestive system:Oral \n", + "1 root:Host-associated:Human:Digestive system:Large intestine \n", + "\n", + " relationships.biome.data.type \n", + "0 biomes \n", + "1 biomes " + ] + }, + "execution_count": 358, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the table containing the results of the query\n", + "resources_df_sp" + ] + }, + { + "cell_type": "markdown", + "id": "b8ba942c-2ca0-44e0-bab0-0e770d78db0f", + "metadata": {}, + "source": [ + "##### Q: how many times does the species occur in the catalogues? In which biomes?" + ] + }, + { + "cell_type": "markdown", + "id": "64b728c6-d3e5-4788-9d5b-72ee569d9f52", + "metadata": { + "tags": [] + }, + "source": [ + "##### Query the database with the genus filter and store the results in a Pandas DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "id": "491f9888-2bef-4823-8e02-4e74c4a908af", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " search_filter = Modifier(f\"taxon_lineage={genus_filter}\")\n", + " resources = map(lambda r: r.json, mgnify.iterate(endpoint, filter=search_filter))\n", + " resources_df = pd.json_normalize(resources)" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "568c1673-91bb-4a7b-b302-308e774bc127", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ena-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.last-updateattributes.first-createdrelationships.catalogue.data.idrelationships.catalogue.data.typerelationships.biome.data.idrelationships.biome.data.typeattributes.ncbi-genome-accessionattributes.ncbi-sample-accessionattributes.ncbi-study-accession
0genomesMGYG00031896717013Europe[Europe]MGYG000318967SAMEA112352570ERP1250743118135637714451.72MAG98.8800000.6800000.000.000.0017.018255789.2184.90d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercoripullorum62717.0803.01914.02023-04-26T10:45:44.1593872023-04-26T10:45:44.156146chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
1genomesMGYG00031751316001Europe[Europe]MGYG000317513SAMEA112351745ERP12507430717754410127647.79MAG98.7000000.1800000.0020.090.0018.023258988.4983.62d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp000433175313656.01064.02592.02023-04-26T10:12:21.2221942023-04-26T10:12:21.219735chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
2genomesMGYG00031737916261Europe[]MGYG000317379SAMEA112348875ERP1250742443261384717148.85MAG80.5300000.8300000.000.000.0010.09199380.6879.13d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella lascolaii1NaNNaNNaN2023-04-26T10:20:51.0416292023-04-26T10:20:51.039628chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
3genomesMGYG00030820117014not provided[]MGYG000308201SAMN15816872SRP1993072869782383939556.37MAG85.1594651.6666670.000.000.0015.014219088.4587.12d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp9005404151NaNNaNNaN2023-04-26T10:45:46.2557062023-04-26T10:45:46.252869chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesGCA_019113095NaNNaN
4genomesMGYG00030802015777not provided[]MGYG000308020SAMN15816846SRP1993072294566734426051.33MAG96.1148651.35135194.120.0010.5620.021194092.2789.95d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella avicola1NaNNaNNaN2023-04-26T10:05:08.4225462023-04-26T10:05:08.418890chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesGCA_019116685NaNNaN
..................................................................................................................
443genomesMGYG0000005346592Oceania[North America, Europe, Asia, Oceania, South America, Africa]MGYG000000534SRS476185SRP0294413142227855349349.62MAG96.8600000.0500000.000.000.0019.020245188.0084.94d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp9005439753107124.0872.06252.02021-12-07T19:07:36.4353372021-12-07T19:07:36.432779human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
444genomesMGYG0000005265736Oceania[Europe, Oceania]MGYG000000526SRS476306SRP02944135100353818253647.78MAG97.7800000.1900000.000.000.0019.017288091.1187.60d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp90077039543119.01696.01423.02021-12-07T18:38:44.0853582021-12-07T18:38:44.061293human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
445genomesMGYG0000002895013Europe[North America, Europe, Asia]MGYG000000289ERS473073ERP0059893001000845235051.55MAG98.4700000.00000094.120.0011.2518.021242788.6385.54d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella buccae745042.0500.04542.02021-12-07T18:12:03.2869752021-12-07T18:12:03.284256human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
446genomesMGYG0000002728133Asia[North America, Europe, Asia, Oceania, South America, Africa]MGYG000000272NaNNaN39910642424882643.02Isolate98.1400001.52000094.9699.6799.5620.036337087.6981.66d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis16217364.0525.016839.02021-12-07T19:49:53.9571292021-12-07T19:49:53.954642human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNSAMN09736551PRJNA482748
447genomesMGYG0000002159337Asia[North America, Europe, Asia, Oceania, South America, Africa, NA]MGYG000000215NaNNaN31176481524005848.90Isolate97.9000000.00000095.8099.6799.4520.035286789.6483.26d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercorea4059388.0567.08821.02021-12-07T20:23:55.4434622021-12-07T20:23:55.440610human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNSAMN09734707PRJNA482748
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448 rows × 37 columns

\n", + "
" + ], + "text/plain": [ + " type id attributes.genome-id \\\n", + "0 genomes MGYG000318967 17013 \n", + "1 genomes MGYG000317513 16001 \n", + "2 genomes MGYG000317379 16261 \n", + "3 genomes MGYG000308201 17014 \n", + "4 genomes MGYG000308020 15777 \n", + ".. ... ... ... \n", + "443 genomes MGYG000000534 6592 \n", + "444 genomes MGYG000000526 5736 \n", + "445 genomes MGYG000000289 5013 \n", + "446 genomes MGYG000000272 8133 \n", + "447 genomes MGYG000000215 9337 \n", + "\n", + " attributes.geographic-origin \\\n", + "0 Europe \n", + "1 Europe \n", + "2 Europe \n", + "3 not provided \n", + "4 not provided \n", + ".. ... \n", + "443 Oceania \n", + "444 Oceania \n", + "445 Europe \n", + "446 Asia \n", + "447 Asia \n", + "\n", + " attributes.geographic-range \\\n", + "0 [Europe] \n", + "1 [Europe] \n", + "2 [] \n", + "3 [] \n", + "4 [] \n", + ".. ... \n", + "443 [North America, Europe, Asia, Oceania, South America, Africa] \n", + "444 [Europe, Oceania] \n", + "445 [North America, Europe, Asia] \n", + "446 [North America, Europe, Asia, Oceania, South America, Africa] \n", + "447 [North America, Europe, Asia, Oceania, South America, Africa, NA] \n", + "\n", + " attributes.accession attributes.ena-sample-accession \\\n", + "0 MGYG000318967 SAMEA112352570 \n", + "1 MGYG000317513 SAMEA112351745 \n", + "2 MGYG000317379 SAMEA112348875 \n", + "3 MGYG000308201 SAMN15816872 \n", + "4 MGYG000308020 SAMN15816846 \n", + ".. ... ... \n", + "443 MGYG000000534 SRS476185 \n", + "444 MGYG000000526 SRS476306 \n", + "445 MGYG000000289 ERS473073 \n", + "446 MGYG000000272 NaN \n", + "447 MGYG000000215 NaN \n", + "\n", + " attributes.ena-study-accession attributes.length attributes.num-contigs \\\n", + "0 ERP125074 3118135 63 \n", + "1 ERP125074 3071775 44 \n", + "2 ERP125074 2443261 384 \n", + "3 SRP199307 2869782 383 \n", + "4 SRP199307 2294566 73 \n", + ".. ... ... ... \n", + "443 SRP029441 3142227 85 \n", + "444 SRP029441 3510035 38 \n", + "445 ERP005989 3001000 84 \n", + "446 NaN 3991064 242 \n", + "447 NaN 3117648 152 \n", + "\n", + " attributes.n-50 attributes.gc-content attributes.type \\\n", + "0 77144 51.72 MAG \n", + "1 101276 47.79 MAG \n", + "2 7171 48.85 MAG \n", + "3 9395 56.37 MAG \n", + "4 44260 51.33 MAG \n", + ".. ... ... ... \n", + "443 53493 49.62 MAG \n", + "444 182536 47.78 MAG \n", + "445 52350 51.55 MAG \n", + "446 48826 43.02 Isolate \n", + "447 40058 48.90 Isolate \n", + "\n", + " attributes.completeness attributes.contamination attributes.rna-5s \\\n", + "0 98.880000 0.680000 0.00 \n", + "1 98.700000 0.180000 0.00 \n", + "2 80.530000 0.830000 0.00 \n", + "3 85.159465 1.666667 0.00 \n", + "4 96.114865 1.351351 94.12 \n", + ".. ... ... ... \n", + "443 96.860000 0.050000 0.00 \n", + "444 97.780000 0.190000 0.00 \n", + "445 98.470000 0.000000 94.12 \n", + "446 98.140000 1.520000 94.96 \n", + "447 97.900000 0.000000 95.80 \n", + "\n", + " attributes.rna-16s attributes.rna-23s attributes.trnas \\\n", + "0 0.00 0.00 17.0 \n", + "1 20.09 0.00 18.0 \n", + "2 0.00 0.00 10.0 \n", + "3 0.00 0.00 15.0 \n", + "4 0.00 10.56 20.0 \n", + ".. ... ... ... \n", + "443 0.00 0.00 19.0 \n", + "444 0.00 0.00 19.0 \n", + "445 0.00 11.25 18.0 \n", + "446 99.67 99.56 20.0 \n", + "447 99.67 99.45 20.0 \n", + "\n", + " attributes.nc-rnas attributes.num-proteins attributes.eggnog-coverage \\\n", + "0 18 2557 89.21 \n", + "1 23 2589 88.49 \n", + "2 9 1993 80.68 \n", + "3 14 2190 88.45 \n", + "4 21 1940 92.27 \n", + ".. ... ... ... \n", + "443 20 2451 88.00 \n", + "444 17 2880 91.11 \n", + "445 21 2427 88.63 \n", + "446 36 3370 87.69 \n", + "447 35 2867 89.64 \n", + "\n", + " attributes.ipr-coverage \\\n", + "0 84.90 \n", + "1 83.62 \n", + "2 79.13 \n", + "3 87.12 \n", + "4 89.95 \n", + ".. ... \n", + "443 84.94 \n", + "444 87.60 \n", + "445 85.54 \n", + "446 81.66 \n", + "447 83.26 \n", + "\n", + " attributes.taxon-lineage \\\n", + "0 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercoripullorum \n", + "1 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp000433175 \n", + "2 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella lascolaii \n", + "3 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900540415 \n", + "4 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella avicola \n", + ".. ... \n", + "443 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900543975 \n", + "444 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900770395 \n", + "445 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella buccae \n", + "446 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis \n", + "447 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercorea \n", + "\n", + " attributes.num-genomes-total attributes.pangenome-size \\\n", + "0 6 2717.0 \n", + "1 31 3656.0 \n", + "2 1 NaN \n", + "3 1 NaN \n", + "4 1 NaN \n", + ".. ... ... \n", + "443 310 7124.0 \n", + "444 4 3119.0 \n", + "445 74 5042.0 \n", + "446 162 17364.0 \n", + "447 405 9388.0 \n", + "\n", + " attributes.pangenome-core-size attributes.pangenome-accessory-size \\\n", + "0 803.0 1914.0 \n", + "1 1064.0 2592.0 \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "443 872.0 6252.0 \n", + "444 1696.0 1423.0 \n", + "445 500.0 4542.0 \n", + "446 525.0 16839.0 \n", + "447 567.0 8821.0 \n", + "\n", + " attributes.last-update attributes.first-created \\\n", + "0 2023-04-26T10:45:44.159387 2023-04-26T10:45:44.156146 \n", + "1 2023-04-26T10:12:21.222194 2023-04-26T10:12:21.219735 \n", + "2 2023-04-26T10:20:51.041629 2023-04-26T10:20:51.039628 \n", + "3 2023-04-26T10:45:46.255706 2023-04-26T10:45:46.252869 \n", + "4 2023-04-26T10:05:08.422546 2023-04-26T10:05:08.418890 \n", + ".. ... ... \n", + "443 2021-12-07T19:07:36.435337 2021-12-07T19:07:36.432779 \n", + "444 2021-12-07T18:38:44.085358 2021-12-07T18:38:44.061293 \n", + "445 2021-12-07T18:12:03.286975 2021-12-07T18:12:03.284256 \n", + "446 2021-12-07T19:49:53.957129 2021-12-07T19:49:53.954642 \n", + "447 2021-12-07T20:23:55.443462 2021-12-07T20:23:55.440610 \n", + "\n", + " relationships.catalogue.data.id relationships.catalogue.data.type \\\n", + "0 chicken-gut-v1-0 genome-catalogues \n", + "1 chicken-gut-v1-0 genome-catalogues \n", + "2 chicken-gut-v1-0 genome-catalogues \n", + "3 chicken-gut-v1-0 genome-catalogues \n", + "4 chicken-gut-v1-0 genome-catalogues \n", + ".. ... ... \n", + "443 human-gut-v2-0-1 genome-catalogues \n", + "444 human-gut-v2-0-1 genome-catalogues \n", + "445 human-gut-v2-0-1 genome-catalogues \n", + "446 human-gut-v2-0-1 genome-catalogues \n", + "447 human-gut-v2-0-1 genome-catalogues \n", + "\n", + " relationships.biome.data.id \\\n", + "0 root:Host-associated:Birds:Digestive system:Digestive tube \n", + "1 root:Host-associated:Birds:Digestive system:Digestive tube \n", + "2 root:Host-associated:Birds:Digestive system:Digestive tube \n", + "3 root:Host-associated:Birds:Digestive system:Digestive tube \n", + "4 root:Host-associated:Birds:Digestive system:Digestive tube \n", + ".. ... \n", + "443 root:Host-associated:Human:Digestive system:Large intestine \n", + "444 root:Host-associated:Human:Digestive system:Large intestine \n", + "445 root:Host-associated:Human:Digestive system:Large intestine \n", + "446 root:Host-associated:Human:Digestive system:Large intestine \n", + "447 root:Host-associated:Human:Digestive system:Large intestine \n", + "\n", + " relationships.biome.data.type attributes.ncbi-genome-accession \\\n", + "0 biomes NaN \n", + "1 biomes NaN \n", + "2 biomes NaN \n", + "3 biomes GCA_019113095 \n", + "4 biomes GCA_019116685 \n", + ".. ... ... \n", + "443 biomes NaN \n", + "444 biomes NaN \n", + "445 biomes NaN \n", + "446 biomes NaN \n", + "447 biomes NaN \n", + "\n", + " attributes.ncbi-sample-accession attributes.ncbi-study-accession \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "443 NaN NaN \n", + "444 NaN NaN \n", + "445 NaN NaN \n", + "446 SAMN09736551 PRJNA482748 \n", + "447 SAMN09734707 PRJNA482748 \n", + "\n", + "[448 rows x 37 columns]" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the table containing the results of the query\n", + "resources_df" + ] + }, + { + "cell_type": "markdown", + "id": "35b45d07-292e-4ed1-860e-90c94ddfdb82", + "metadata": {}, + "source": [ + "##### Q: how many times does the genus occur in MGnify Genomes?" + ] + }, + { + "cell_type": "markdown", + "id": "e73d3b6c-5651-4db3-b877-d1eb4e9639fd", + "metadata": {}, + "source": [ + "Let's get more information on the occurrence of this genus in MGnify Genomes." + ] + }, + { + "cell_type": "markdown", + "id": "cd609c69-de91-455c-8029-ac95179be2fc", + "metadata": {}, + "source": [ + "##### Which catalogues does the genus occur in?\n", + "We can use the information from the ```relationships.catalogue.data.id``` column to generate a pie chart that shows the catalogues where the genus can be found:" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "be264754-f3f9-4f34-bd53-7885c88a1d04", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "resources_df.groupby('relationships.catalogue.data.id').size().plot(kind='pie', autopct='%1.0f%%')\n", + "plt.title('Catalogues where the genus Prevotella is found');" + ] + }, + { + "cell_type": "markdown", + "id": "29a1fe11-6493-4869-95fb-95fcde52073b", + "metadata": {}, + "source": [ + "##### What are the most common species from the Prevotella genus across the catalogues?" + ] + }, + { + "cell_type": "markdown", + "id": "5f613569-d0cc-4fa6-8b42-d56f86980510", + "metadata": {}, + "source": [ + "First, let's see what the total number of distinct lineages are within the Prevotella genus across the catalogues:" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "id": "03fd27c3-c3bb-4d3a-aee1-6cd2d256bef0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "306" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resources_df['attributes.taxon-lineage'].nunique()" + ] + }, + { + "cell_type": "markdown", + "id": "d7542a48-d4a2-4e3b-a3d9-af610a9ec24a", + "metadata": {}, + "source": [ + "Now let's find 10 most common species:" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "fa81e906-e898-424d-ad8a-6a7b639c93b0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__ 87\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002300055 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002251295 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900316565 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900313215 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002299275 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900548195 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002251365 3\n", + "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002480935 3\n", + "Name: attributes.taxon-lineage, dtype: int64" + ] + }, + "execution_count": 213, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resources_df['attributes.taxon-lineage'].value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "7267bc06-5cb7-4e40-aa59-ca39d0b641e1", + "metadata": {}, + "source": [ + "The top record occurs in the catalogues 87 times but there is no species name (the name is ```s__``` indicating that it could not be assigned by GTDB-Tk, the tool that we use for taxonomic analysis). This indicates that the species is not yet known in GTDB. \n", + "\n", + "Note: the 87 records represent multiple unknown (novel) species. In other words, across the catalogues we have 87\n", + "species representative genomes that belong to the genus Prevotella but we don't know what species they are.\n", + "\n", + "The next most common species occurs in the catalogues 3 times and there are at least 9 species like that as we can see from the table (there are likely more but we only printed the first 10 lines). Since our catalogues are dereplicated at the species level, each species can only be found in a biome-specific catalogue once. In total we currently have 9 catalogues so we wouldn't expect the maximum number of occurences for any species to exceed 9." + ] + }, + { + "cell_type": "markdown", + "id": "1a4bf1bc-afb5-4c19-81a2-a3d51130edac", + "metadata": {}, + "source": [ + "##### Which catalogues contain the novel Prevotella species?" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "2664b705-3904-4c40-9ca0-701d12892398", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Save the rows containing novel Prevotella species into a new dataframe\n", + "novel_prevotella_df = resources_df[resources_df['attributes.taxon-lineage'] == 'd__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__']\n", + "# Plot the catalogues\n", + "novel_prevotella_df.groupby('relationships.catalogue.data.id').size().plot(kind='pie', autopct='%1.0f%%')\n", + "plt.title('Catalogues where the novel Prevotella species are found');" + ] + }, + { + "cell_type": "markdown", + "id": "93ad9b03-9cd6-411a-986a-625cb5f9afea", + "metadata": {}, + "source": [ + "##### Q: Which catalogue has a higher number of novel Prevotella species? \n", + "\n", + "##### Q: Which catalogue only contains known Prevotella species?\n", + "Hint: compare this pie chart to the previous one - which catalogue disappeared?" + ] + }, + { + "cell_type": "markdown", + "id": "eacde0da-c37f-4681-889d-f50b6b5112c1", + "metadata": {}, + "source": [ + "#### Task 3: Create a graph showing most common phyla in MGnify Genomes." + ] + }, + { + "cell_type": "markdown", + "id": "cbae69d4-594e-414d-be2a-8a5383d091a7", + "metadata": {}, + "source": [ + "For this exercise we will be working with pre-fetched data for all genomes. This is because fetching it takes a while given the number of genomes but if you were fetching it yourself, the code for that is below (expand by clicking on the three dots but please don't execute the code)." + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "id": "d576840c-5535-4958-8031-5952c66532b8", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ncbi-genome-accessionattributes.ncbi-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.last-updateattributes.first-createdrelationships.biome.data.idrelationships.biome.data.typerelationships.catalogue.data.idrelationships.catalogue.data.typeattributes.ena-study-accessionattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.ncbi-sample-accession
0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNaNNaNNaNNaNNaN
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415NaN20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0NaN
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325NaN22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0NaN
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035NaN25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0NaN
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965NaN25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0NaN
..................................................................................................................
12525genomesMGYG0000000058452Europe[North America, Europe]MGYG000000005ERS417217NaNNaN39304283235003228.59Isolate99.300.0089.0899.6799.8320.067375594.3088.95d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A22021-12-07T19:58:48.5266952021-12-07T19:58:48.523776root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243622.03040.0582.0NaN
12526genomesMGYG0000000048822Europe[North America, Europe, Asia, NA]MGYG000000004ERS370072NaNNaN36988961059029754.19Isolate98.660.2293.2899.6799.8320.036363495.7186.35d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis252021-12-07T20:09:19.7438372021-12-07T20:09:19.741171root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056245274.0920.04354.0NaN
12527genomesMGYG0000000037373Europe[North America, Europe, Asia, Oceania, South America, NA]MGYG000000003ERS370070NaNNaN32295183515857058.52Isolate100.000.0094.9699.6799.6620.027269093.3889.14d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii11812021-12-07T19:28:56.8140092021-12-07T19:28:56.809967root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562414402.01163.013239.0NaN
12528genomesMGYG0000000029258Europe[North America, Europe, Asia, Oceania, South America]MGYG000000002ERS370064NaNNaN443310910110926742.60Isolate99.370.0082.3595.3786.1919.057392095.5688.85d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis3592021-12-07T20:21:43.8294182021-12-07T20:21:43.826838root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562428872.0661.028211.0NaN
12529genomesMGYG0000000014888Europe[North America, Europe]MGYG000000001ERS370061NaNNaN32196171374725828.26Isolate98.590.7088.2499.7499.8320.063318293.7886.42d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp90236236542021-12-07T18:06:34.7657822021-12-07T18:06:34.762463root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243154.01350.01804.0NaN
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12530 rows × 37 columns

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" + ], + "text/plain": [ + " type id attributes.genome-id \\\n", + "0 genomes MGYG000321626 17285 \n", + "1 genomes MGYG000321625 17333 \n", + "2 genomes MGYG000321620 17338 \n", + "3 genomes MGYG000321606 17269 \n", + "4 genomes MGYG000321602 17200 \n", + "... ... ... ... \n", + "12525 genomes MGYG000000005 8452 \n", + "12526 genomes MGYG000000004 8822 \n", + "12527 genomes MGYG000000003 7373 \n", + "12528 genomes MGYG000000002 9258 \n", + "12529 genomes MGYG000000001 4888 \n", + "\n", + " attributes.geographic-origin \\\n", + "0 Europe \n", + "1 Asia \n", + "2 Asia \n", + "3 Asia \n", + "4 Asia \n", + "... ... \n", + "12525 Europe \n", + "12526 Europe \n", + "12527 Europe \n", + "12528 Europe \n", + "12529 Europe \n", + "\n", + " attributes.geographic-range \\\n", + "0 [] \n", + "1 [Asia] \n", + "2 [Asia] \n", + "3 [Asia] \n", + "4 [Asia] \n", + "... ... \n", + "12525 [North America, Europe] \n", + "12526 [North America, Europe, Asia, NA] \n", + "12527 [North America, Europe, Asia, Oceania, South America, NA] \n", + "12528 [North America, Europe, Asia, Oceania, South America] \n", + "12529 [North America, Europe] \n", + "\n", + " attributes.accession attributes.ena-sample-accession \\\n", + "0 MGYG000321626 SAMN33097420 \n", + "1 MGYG000321625 SAMN18106362 \n", + "2 MGYG000321620 SAMN18106366 \n", + "3 MGYG000321606 SAMN18106382 \n", + "4 MGYG000321602 SAMN18106384 \n", + "... ... ... \n", + "12525 MGYG000000005 ERS417217 \n", + "12526 MGYG000000004 ERS370072 \n", + "12527 MGYG000000003 ERS370070 \n", + "12528 MGYG000000002 ERS370064 \n", + "12529 MGYG000000001 ERS370061 \n", + "\n", + " attributes.ncbi-genome-accession attributes.ncbi-study-accession \\\n", + "0 GCA_028655675 PRJNA931735 \n", + "1 GCA_026537415 NaN \n", + "2 GCA_026537325 NaN \n", + "3 GCA_026537035 NaN \n", + "4 GCA_026536965 NaN \n", + "... ... ... \n", + "12525 NaN NaN \n", + "12526 NaN NaN \n", + "12527 NaN NaN \n", + "12528 NaN NaN \n", + "12529 NaN NaN \n", + "\n", + " attributes.length attributes.num-contigs attributes.n-50 \\\n", + "0 1881475 145 32562 \n", + "1 2042703 39 281954 \n", + "2 2224927 19 390684 \n", + "3 2522333 48 138263 \n", + "4 2528524 34 177044 \n", + "... ... ... ... \n", + "12525 3930428 32 350032 \n", + "12526 3698896 105 90297 \n", + "12527 3229518 35 158570 \n", + "12528 4433109 101 109267 \n", + "12529 3219617 137 47258 \n", + "\n", + " attributes.gc-content attributes.type attributes.completeness \\\n", + "0 49.81 Isolate 99.03 \n", + "1 55.50 MAG 100.00 \n", + "2 59.87 MAG 97.64 \n", + "3 34.23 MAG 98.87 \n", + "4 35.11 MAG 98.31 \n", + "... ... ... ... \n", + "12525 28.59 Isolate 99.30 \n", + "12526 54.19 Isolate 98.66 \n", + "12527 58.52 Isolate 100.00 \n", + "12528 42.60 Isolate 99.37 \n", + "12529 28.26 Isolate 98.59 \n", + "\n", + " attributes.contamination attributes.rna-5s attributes.rna-16s \\\n", + "0 0.00 93.28 99.41 \n", + "1 0.00 0.00 99.61 \n", + "2 1.78 0.00 99.67 \n", + "3 0.56 94.96 99.67 \n", + "4 0.00 94.96 99.67 \n", + "... ... ... ... \n", + "12525 0.00 89.08 99.67 \n", + "12526 0.22 93.28 99.67 \n", + "12527 0.00 94.96 99.67 \n", + "12528 0.00 82.35 95.37 \n", + "12529 0.70 88.24 99.74 \n", + "\n", + " attributes.rna-23s attributes.trnas attributes.nc-rnas \\\n", + "0 97.64 20.0 26 \n", + "1 99.28 20.0 21 \n", + "2 99.18 20.0 14 \n", + "3 99.86 19.0 26 \n", + "4 99.86 20.0 32 \n", + "... ... ... ... \n", + "12525 99.83 20.0 67 \n", + "12526 99.83 20.0 36 \n", + "12527 99.66 20.0 27 \n", + "12528 86.19 19.0 57 \n", + "12529 99.83 20.0 63 \n", + "\n", + " attributes.num-proteins attributes.eggnog-coverage \\\n", + "0 1803 95.73 \n", + "1 1842 89.36 \n", + "2 1811 95.42 \n", + "3 2241 95.85 \n", + "4 2249 94.49 \n", + "... ... ... \n", + "12525 3755 94.30 \n", + "12526 3634 95.71 \n", + "12527 2690 93.38 \n", + "12528 3920 95.56 \n", + "12529 3182 93.78 \n", + "\n", + " attributes.ipr-coverage \\\n", + "0 89.24 \n", + "1 89.03 \n", + "2 93.04 \n", + "3 92.64 \n", + "4 92.26 \n", + "... ... \n", + "12525 88.95 \n", + "12526 86.35 \n", + "12527 89.14 \n", + "12528 88.85 \n", + "12529 86.42 \n", + "\n", + " attributes.taxon-lineage \\\n", + "0 d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii \n", + "1 d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__ \n", + "2 d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__ \n", + "3 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075 \n", + "4 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E \n", + "... ... \n", + "12525 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A \n", + "12526 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis \n", + "12527 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii \n", + "12528 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis \n", + "12529 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp902362365 \n", + "\n", + " attributes.num-genomes-total attributes.last-update \\\n", + "0 1 2023-07-07T11:35:45.488929 \n", + "1 2 2023-07-07T11:37:17.515368 \n", + "2 7 2023-07-07T11:37:26.626388 \n", + "3 12 2023-07-07T11:35:14.099161 \n", + "4 19 2023-07-07T11:33:02.552198 \n", + "... ... ... \n", + "12525 2 2021-12-07T19:58:48.526695 \n", + "12526 25 2021-12-07T20:09:19.743837 \n", + "12527 1181 2021-12-07T19:28:56.814009 \n", + "12528 359 2021-12-07T20:21:43.829418 \n", + "12529 4 2021-12-07T18:06:34.765782 \n", + "\n", + " attributes.first-created \\\n", + "0 2023-07-07T11:35:45.486483 \n", + "1 2023-07-07T11:37:17.512635 \n", + "2 2023-07-07T11:37:26.624241 \n", + "3 2023-07-07T11:35:14.096893 \n", + "4 2023-07-07T11:33:02.549672 \n", + "... ... \n", + "12525 2021-12-07T19:58:48.523776 \n", + "12526 2021-12-07T20:09:19.741171 \n", + "12527 2021-12-07T19:28:56.809967 \n", + "12528 2021-12-07T20:21:43.826838 \n", + "12529 2021-12-07T18:06:34.762463 \n", + "\n", + " relationships.biome.data.id \\\n", + "0 root:Host-associated:Insecta:Digestive system \n", + "1 root:Host-associated:Insecta:Digestive system \n", + "2 root:Host-associated:Insecta:Digestive system \n", + "3 root:Host-associated:Insecta:Digestive system \n", + "4 root:Host-associated:Insecta:Digestive system \n", + "... ... \n", + "12525 root:Host-associated:Human:Digestive system:Large intestine \n", + "12526 root:Host-associated:Human:Digestive system:Large intestine \n", + "12527 root:Host-associated:Human:Digestive system:Large intestine \n", + "12528 root:Host-associated:Human:Digestive system:Large intestine \n", + "12529 root:Host-associated:Human:Digestive system:Large intestine \n", + "\n", + " relationships.biome.data.type relationships.catalogue.data.id \\\n", + "0 biomes honeybee-gut-v1-0 \n", + "1 biomes honeybee-gut-v1-0 \n", + "2 biomes honeybee-gut-v1-0 \n", + "3 biomes honeybee-gut-v1-0 \n", + "4 biomes honeybee-gut-v1-0 \n", + "... ... ... \n", + "12525 biomes human-gut-v2-0-1 \n", + "12526 biomes human-gut-v2-0-1 \n", + "12527 biomes human-gut-v2-0-1 \n", + "12528 biomes human-gut-v2-0-1 \n", + "12529 biomes human-gut-v2-0-1 \n", + "\n", + " relationships.catalogue.data.type attributes.ena-study-accession \\\n", + "0 genome-catalogues NaN \n", + "1 genome-catalogues SRP308969 \n", + "2 genome-catalogues SRP308969 \n", + "3 genome-catalogues SRP308969 \n", + "4 genome-catalogues SRP308969 \n", + "... ... ... \n", + "12525 genome-catalogues ERP105624 \n", + "12526 genome-catalogues ERP105624 \n", + "12527 genome-catalogues ERP105624 \n", + "12528 genome-catalogues ERP105624 \n", + "12529 genome-catalogues ERP105624 \n", + "\n", + " attributes.pangenome-size attributes.pangenome-core-size \\\n", + "0 NaN NaN \n", + "1 1735.0 1688.0 \n", + "2 2230.0 1526.0 \n", + "3 2444.0 1872.0 \n", + "4 3216.0 1678.0 \n", + "... ... ... \n", + "12525 3622.0 3040.0 \n", + "12526 5274.0 920.0 \n", + "12527 14402.0 1163.0 \n", + "12528 28872.0 661.0 \n", + "12529 3154.0 1350.0 \n", + "\n", + " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \n", + "0 NaN NaN \n", + "1 47.0 NaN \n", + "2 704.0 NaN \n", + "3 572.0 NaN \n", + "4 1538.0 NaN \n", + "... ... ... \n", + "12525 582.0 NaN \n", + "12526 4354.0 NaN \n", + "12527 13239.0 NaN \n", + "12528 28211.0 NaN \n", + "12529 1804.0 NaN \n", + "\n", + "[12530 rows x 37 columns]" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "endpoint = \"genomes\"\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " resources_all = map(lambda r: r.json, mgnify.iterate(endpoint))\n", + "resources_all_df = pd.json_normalize(resources_all)\n", + "resources_all_df.to_parquet('all_genome_resources_Aug2023.parquet')" + ] + }, + { + "cell_type": "markdown", + "id": "75956486-d5a3-46dd-aaa0-0fa37fb9b94e", + "metadata": {}, + "source": [ + "The table with all genomes has been saved as a Parquet file. This file format allows for efficient data storage and retrieval.\n", + "The file is located in ../example-data/genomes/ directory and is called ```all_genome_resources_Aug2023.parquet```.\n", + "\n", + "Let's load it into a dataframe ```all_genomes_df```:" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "id": "b3aa6767-a07a-4c61-88aa-011183b6fb68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_genomes_df = pd.read_parquet('../example-data/genomes/all_genome_resources_Aug2023.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "id": "96ecd56e-6208-4b2c-bba9-72ea839b12ce", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ncbi-genome-accessionattributes.ncbi-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.last-updateattributes.first-createdrelationships.biome.data.idrelationships.biome.data.typerelationships.catalogue.data.idrelationships.catalogue.data.typeattributes.ena-study-accessionattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.ncbi-sample-accession
0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNoneNaNNaNNaNNone
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415None20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0None
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325None22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0None
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035None25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0None
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965None25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0None
\n", + "
" + ], + "text/plain": [ + " type id attributes.genome-id attributes.geographic-origin \\\n", + "0 genomes MGYG000321626 17285 Europe \n", + "1 genomes MGYG000321625 17333 Asia \n", + "2 genomes MGYG000321620 17338 Asia \n", + "3 genomes MGYG000321606 17269 Asia \n", + "4 genomes MGYG000321602 17200 Asia \n", + "\n", + " attributes.geographic-range attributes.accession \\\n", + "0 [] MGYG000321626 \n", + "1 [Asia] MGYG000321625 \n", + "2 [Asia] MGYG000321620 \n", + "3 [Asia] MGYG000321606 \n", + "4 [Asia] MGYG000321602 \n", + "\n", + " attributes.ena-sample-accession attributes.ncbi-genome-accession \\\n", + "0 SAMN33097420 GCA_028655675 \n", + "1 SAMN18106362 GCA_026537415 \n", + "2 SAMN18106366 GCA_026537325 \n", + "3 SAMN18106382 GCA_026537035 \n", + "4 SAMN18106384 GCA_026536965 \n", + "\n", + " attributes.ncbi-study-accession attributes.length attributes.num-contigs \\\n", + "0 PRJNA931735 1881475 145 \n", + "1 None 2042703 39 \n", + "2 None 2224927 19 \n", + "3 None 2522333 48 \n", + "4 None 2528524 34 \n", + "\n", + " attributes.n-50 attributes.gc-content attributes.type \\\n", + "0 32562 49.81 Isolate \n", + "1 281954 55.50 MAG \n", + "2 390684 59.87 MAG \n", + "3 138263 34.23 MAG \n", + "4 177044 35.11 MAG \n", + "\n", + " attributes.completeness attributes.contamination attributes.rna-5s \\\n", + "0 99.03 0.00 93.28 \n", + "1 100.00 0.00 0.00 \n", + "2 97.64 1.78 0.00 \n", + "3 98.87 0.56 94.96 \n", + "4 98.31 0.00 94.96 \n", + "\n", + " attributes.rna-16s attributes.rna-23s attributes.trnas \\\n", + "0 99.41 97.64 20.0 \n", + "1 99.61 99.28 20.0 \n", + "2 99.67 99.18 20.0 \n", + "3 99.67 99.86 19.0 \n", + "4 99.67 99.86 20.0 \n", + "\n", + " attributes.nc-rnas attributes.num-proteins attributes.eggnog-coverage \\\n", + "0 26 1803 95.73 \n", + "1 21 1842 89.36 \n", + "2 14 1811 95.42 \n", + "3 26 2241 95.85 \n", + "4 32 2249 94.49 \n", + "\n", + " attributes.ipr-coverage \\\n", + "0 89.24 \n", + "1 89.03 \n", + "2 93.04 \n", + "3 92.64 \n", + "4 92.26 \n", + "\n", + " attributes.taxon-lineage \\\n", + "0 d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii \n", + "1 d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__ \n", + "2 d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__ \n", + "3 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075 \n", + "4 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E \n", + "\n", + " attributes.num-genomes-total attributes.last-update \\\n", + "0 1 2023-07-07T11:35:45.488929 \n", + "1 2 2023-07-07T11:37:17.515368 \n", + "2 7 2023-07-07T11:37:26.626388 \n", + "3 12 2023-07-07T11:35:14.099161 \n", + "4 19 2023-07-07T11:33:02.552198 \n", + "\n", + " attributes.first-created relationships.biome.data.id \\\n", + "0 2023-07-07T11:35:45.486483 root:Host-associated:Insecta:Digestive system \n", + "1 2023-07-07T11:37:17.512635 root:Host-associated:Insecta:Digestive system \n", + "2 2023-07-07T11:37:26.624241 root:Host-associated:Insecta:Digestive system \n", + "3 2023-07-07T11:35:14.096893 root:Host-associated:Insecta:Digestive system \n", + "4 2023-07-07T11:33:02.549672 root:Host-associated:Insecta:Digestive system \n", + "\n", + " relationships.biome.data.type relationships.catalogue.data.id \\\n", + "0 biomes honeybee-gut-v1-0 \n", + "1 biomes honeybee-gut-v1-0 \n", + "2 biomes honeybee-gut-v1-0 \n", + "3 biomes honeybee-gut-v1-0 \n", + "4 biomes honeybee-gut-v1-0 \n", + "\n", + " relationships.catalogue.data.type attributes.ena-study-accession \\\n", + "0 genome-catalogues None \n", + "1 genome-catalogues SRP308969 \n", + "2 genome-catalogues SRP308969 \n", + "3 genome-catalogues SRP308969 \n", + "4 genome-catalogues SRP308969 \n", + "\n", + " attributes.pangenome-size attributes.pangenome-core-size \\\n", + "0 NaN NaN \n", + "1 1735.0 1688.0 \n", + "2 2230.0 1526.0 \n", + "3 2444.0 1872.0 \n", + "4 3216.0 1678.0 \n", + "\n", + " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \n", + "0 NaN None \n", + "1 47.0 None \n", + "2 704.0 None \n", + "3 572.0 None \n", + "4 1538.0 None " + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's check what this dataframe looks like\n", + "all_genomes_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "id": "6e0baef0-f81e-44f3-bac4-69a11ca27c96", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 12530.000000\n", + "mean 47.652266\n", + "std 10.627749\n", + "min 22.940000\n", + "25% 40.070000\n", + "50% 48.240000\n", + "75% 56.230000\n", + "max 74.840000\n", + "Name: attributes.gc-content, dtype: float64" + ] + }, + "execution_count": 359, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using Pandas we could also do some quick analysis, for example, what is the average GC content of the \n", + "# genomes in MGnify? We can use the .describe() method that calculates statistical information.\n", + "all_genomes_df['attributes.gc-content'].describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b21346da-7af6-4c1c-86fc-20a62da31f7c", + "metadata": {}, + "source": [ + "This is telling us that in total there are 12,530 GC content records (matching the total number of species representative genomes) and the mean GC% is 47.65%.\n", + "\n", + "Now let's plot the phyla. We need to do some prep work first:" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "a5fc7b37-3f13-4303-866a-bd9b572edc2e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Split taxon lineage into columns (separate column for each taxonomic level)\n", + "\n", + "# Write each possible taxonomic level into a list called \"features\"\n", + "features = ['domain', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n", + "\n", + "\n", + "def get_lineage_column(lineage_str, i):\n", + " # This is a Python function that takes in a full taxonomic lineage, for example,\n", + " # d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii\n", + " # and returns the value in the requested position. In Python we count from 0 so \n", + " # if we requested the taxon name in position 1 (i = 1), the function would \n", + " # return \"p__Bacillota\"\n", + "\n", + " lineage_split = lineage_str.split(';')\n", + " return lineage_split[i] \n", + "\n", + "\n", + "# Apply the function to create the new columns in the pandas DataFrame by splitting column attributes.taxon-lineage\n", + "for i, feature in enumerate(features):\n", + " all_genomes_df[feature] = all_genomes_df['attributes.taxon-lineage'].apply(lambda x: get_lineage_column(x, i))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "id": "93a7404a-e8e2-4427-b1da-497b94839e5d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ncbi-genome-accessionattributes.ncbi-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.last-updateattributes.first-createdrelationships.biome.data.idrelationships.biome.data.typerelationships.catalogue.data.idrelationships.catalogue.data.typeattributes.ena-study-accessionattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.ncbi-sample-accessiondomainphylumclassorderfamilygenusspecies
0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNoneNaNNaNNaNNoned__Bacteriap__Bacillotac__Bacillio__Lactobacillalesf__Lactobacillaceaeg__Lactobacilluss__Lactobacillus delbrueckii
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415None20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0Noned__Bacteriap__Pseudomonadotac__Alphaproteobacteriao__Acetobacteralesf__Acetobacteraceaeg__Bombellas__
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325None22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0Noned__Bacteriap__Actinomycetotac__Actinomycetiao__Actinomycetalesf__Bifidobacteriaceaeg__Bifidobacteriums__
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035None25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0Noned__Bacteriap__Pseudomonadotac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceaeg__Gilliamellas__Gilliamella sp945273075
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965None25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0Noned__Bacteriap__Pseudomonadotac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceaeg__Gilliamellas__Gilliamella apicola_E
.......................................................................................................................................
12525genomesMGYG0000000058452Europe[North America, Europe]MGYG000000005ERS417217NoneNone39304283235003228.59Isolate99.300.0089.0899.6799.8320.067375594.3088.95d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A22021-12-07T19:58:48.5266952021-12-07T19:58:48.523776root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243622.03040.0582.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Peptostreptococcalesf__Peptostreptococcaceaeg__Terrisporobacters__Terrisporobacter glycolicus_A
12526genomesMGYG0000000048822Europe[North America, Europe, Asia, NA]MGYG000000004ERS370072NoneNone36988961059029754.19Isolate98.660.2293.2899.6799.8320.036363495.7186.35d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis252021-12-07T20:09:19.7438372021-12-07T20:09:19.741171root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056245274.0920.04354.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Oscillospiralesf__Ruminococcaceaeg__Anaerotruncuss__Anaerotruncus colihominis
12527genomesMGYG0000000037373Europe[North America, Europe, Asia, Oceania, South America, NA]MGYG000000003ERS370070NoneNone32295183515857058.52Isolate100.000.0094.9699.6799.6620.027269093.3889.14d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii11812021-12-07T19:28:56.8140092021-12-07T19:28:56.809967root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562414402.01163.013239.0Noned__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Rikenellaceaeg__Alistipess__Alistipes shahii
12528genomesMGYG0000000029258Europe[North America, Europe, Asia, Oceania, South America]MGYG000000002ERS370064NoneNone443310910110926742.60Isolate99.370.0082.3595.3786.1919.057392095.5688.85d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis3592021-12-07T20:21:43.8294182021-12-07T20:21:43.826838root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562428872.0661.028211.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Lachnospiralesf__Lachnospiraceaeg__Blautia_As__Blautia_A faecis
12529genomesMGYG0000000014888Europe[North America, Europe]MGYG000000001ERS370061NoneNone32196171374725828.26Isolate98.590.7088.2499.7499.8320.063318293.7886.42d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp90236236542021-12-07T18:06:34.7657822021-12-07T18:06:34.762463root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243154.01350.01804.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Peptostreptococcalesf__Peptostreptococcaceaeg__GCA-900066495s__GCA-900066495 sp902362365
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12530 rows × 44 columns

\n", + "
" + ], + "text/plain": [ + " type id attributes.genome-id \\\n", + "0 genomes MGYG000321626 17285 \n", + "1 genomes MGYG000321625 17333 \n", + "2 genomes MGYG000321620 17338 \n", + "3 genomes MGYG000321606 17269 \n", + "4 genomes MGYG000321602 17200 \n", + "... ... ... ... \n", + "12525 genomes MGYG000000005 8452 \n", + "12526 genomes MGYG000000004 8822 \n", + "12527 genomes MGYG000000003 7373 \n", + "12528 genomes MGYG000000002 9258 \n", + "12529 genomes MGYG000000001 4888 \n", + "\n", + " attributes.geographic-origin \\\n", + "0 Europe \n", + "1 Asia \n", + "2 Asia \n", + "3 Asia \n", + "4 Asia \n", + "... ... \n", + "12525 Europe \n", + "12526 Europe \n", + "12527 Europe \n", + "12528 Europe \n", + "12529 Europe \n", + "\n", + " attributes.geographic-range \\\n", + "0 [] \n", + "1 [Asia] \n", + "2 [Asia] \n", + "3 [Asia] \n", + "4 [Asia] \n", + "... ... \n", + "12525 [North America, Europe] \n", + "12526 [North America, Europe, Asia, NA] \n", + "12527 [North America, Europe, Asia, Oceania, South America, NA] \n", + "12528 [North America, Europe, Asia, Oceania, South America] \n", + "12529 [North America, Europe] \n", + "\n", + " attributes.accession attributes.ena-sample-accession \\\n", + "0 MGYG000321626 SAMN33097420 \n", + "1 MGYG000321625 SAMN18106362 \n", + "2 MGYG000321620 SAMN18106366 \n", + "3 MGYG000321606 SAMN18106382 \n", + "4 MGYG000321602 SAMN18106384 \n", + "... ... ... \n", + "12525 MGYG000000005 ERS417217 \n", + "12526 MGYG000000004 ERS370072 \n", + "12527 MGYG000000003 ERS370070 \n", + "12528 MGYG000000002 ERS370064 \n", + "12529 MGYG000000001 ERS370061 \n", + "\n", + " attributes.ncbi-genome-accession attributes.ncbi-study-accession \\\n", + "0 GCA_028655675 PRJNA931735 \n", + "1 GCA_026537415 None \n", + "2 GCA_026537325 None \n", + "3 GCA_026537035 None \n", + "4 GCA_026536965 None \n", + "... ... ... \n", + "12525 None None \n", + "12526 None None \n", + "12527 None None \n", + "12528 None None \n", + "12529 None None \n", + "\n", + " attributes.length attributes.num-contigs attributes.n-50 \\\n", + "0 1881475 145 32562 \n", + "1 2042703 39 281954 \n", + "2 2224927 19 390684 \n", + "3 2522333 48 138263 \n", + "4 2528524 34 177044 \n", + "... ... ... ... \n", + "12525 3930428 32 350032 \n", + "12526 3698896 105 90297 \n", + "12527 3229518 35 158570 \n", + "12528 4433109 101 109267 \n", + "12529 3219617 137 47258 \n", + "\n", + " attributes.gc-content attributes.type attributes.completeness \\\n", + "0 49.81 Isolate 99.03 \n", + "1 55.50 MAG 100.00 \n", + "2 59.87 MAG 97.64 \n", + "3 34.23 MAG 98.87 \n", + "4 35.11 MAG 98.31 \n", + "... ... ... ... \n", + "12525 28.59 Isolate 99.30 \n", + "12526 54.19 Isolate 98.66 \n", + "12527 58.52 Isolate 100.00 \n", + "12528 42.60 Isolate 99.37 \n", + "12529 28.26 Isolate 98.59 \n", + "\n", + " attributes.contamination attributes.rna-5s attributes.rna-16s \\\n", + "0 0.00 93.28 99.41 \n", + "1 0.00 0.00 99.61 \n", + "2 1.78 0.00 99.67 \n", + "3 0.56 94.96 99.67 \n", + "4 0.00 94.96 99.67 \n", + "... ... ... ... \n", + "12525 0.00 89.08 99.67 \n", + "12526 0.22 93.28 99.67 \n", + "12527 0.00 94.96 99.67 \n", + "12528 0.00 82.35 95.37 \n", + "12529 0.70 88.24 99.74 \n", + "\n", + " attributes.rna-23s attributes.trnas attributes.nc-rnas \\\n", + "0 97.64 20.0 26 \n", + "1 99.28 20.0 21 \n", + "2 99.18 20.0 14 \n", + "3 99.86 19.0 26 \n", + "4 99.86 20.0 32 \n", + "... ... ... ... \n", + "12525 99.83 20.0 67 \n", + "12526 99.83 20.0 36 \n", + "12527 99.66 20.0 27 \n", + "12528 86.19 19.0 57 \n", + "12529 99.83 20.0 63 \n", + "\n", + " attributes.num-proteins attributes.eggnog-coverage \\\n", + "0 1803 95.73 \n", + "1 1842 89.36 \n", + "2 1811 95.42 \n", + "3 2241 95.85 \n", + "4 2249 94.49 \n", + "... ... ... \n", + "12525 3755 94.30 \n", + "12526 3634 95.71 \n", + "12527 2690 93.38 \n", + "12528 3920 95.56 \n", + "12529 3182 93.78 \n", + "\n", + " attributes.ipr-coverage \\\n", + "0 89.24 \n", + "1 89.03 \n", + "2 93.04 \n", + "3 92.64 \n", + "4 92.26 \n", + "... ... \n", + "12525 88.95 \n", + "12526 86.35 \n", + "12527 89.14 \n", + "12528 88.85 \n", + "12529 86.42 \n", + "\n", + " attributes.taxon-lineage \\\n", + "0 d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii \n", + "1 d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__ \n", + "2 d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__ \n", + "3 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075 \n", + "4 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E \n", + "... ... \n", + "12525 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A \n", + "12526 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis \n", + "12527 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii \n", + "12528 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis \n", + "12529 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp902362365 \n", + "\n", + " attributes.num-genomes-total attributes.last-update \\\n", + "0 1 2023-07-07T11:35:45.488929 \n", + "1 2 2023-07-07T11:37:17.515368 \n", + "2 7 2023-07-07T11:37:26.626388 \n", + "3 12 2023-07-07T11:35:14.099161 \n", + "4 19 2023-07-07T11:33:02.552198 \n", + "... ... ... \n", + "12525 2 2021-12-07T19:58:48.526695 \n", + "12526 25 2021-12-07T20:09:19.743837 \n", + "12527 1181 2021-12-07T19:28:56.814009 \n", + "12528 359 2021-12-07T20:21:43.829418 \n", + "12529 4 2021-12-07T18:06:34.765782 \n", + "\n", + " attributes.first-created \\\n", + "0 2023-07-07T11:35:45.486483 \n", + "1 2023-07-07T11:37:17.512635 \n", + "2 2023-07-07T11:37:26.624241 \n", + "3 2023-07-07T11:35:14.096893 \n", + "4 2023-07-07T11:33:02.549672 \n", + "... ... \n", + "12525 2021-12-07T19:58:48.523776 \n", + "12526 2021-12-07T20:09:19.741171 \n", + "12527 2021-12-07T19:28:56.809967 \n", + "12528 2021-12-07T20:21:43.826838 \n", + "12529 2021-12-07T18:06:34.762463 \n", + "\n", + " relationships.biome.data.id \\\n", + "0 root:Host-associated:Insecta:Digestive system \n", + "1 root:Host-associated:Insecta:Digestive system \n", + "2 root:Host-associated:Insecta:Digestive system \n", + "3 root:Host-associated:Insecta:Digestive system \n", + "4 root:Host-associated:Insecta:Digestive system \n", + "... ... \n", + "12525 root:Host-associated:Human:Digestive system:Large intestine \n", + "12526 root:Host-associated:Human:Digestive system:Large intestine \n", + "12527 root:Host-associated:Human:Digestive system:Large intestine \n", + "12528 root:Host-associated:Human:Digestive system:Large intestine \n", + "12529 root:Host-associated:Human:Digestive system:Large intestine \n", + "\n", + " relationships.biome.data.type relationships.catalogue.data.id \\\n", + "0 biomes honeybee-gut-v1-0 \n", + "1 biomes honeybee-gut-v1-0 \n", + "2 biomes honeybee-gut-v1-0 \n", + "3 biomes honeybee-gut-v1-0 \n", + "4 biomes honeybee-gut-v1-0 \n", + "... ... ... \n", + "12525 biomes human-gut-v2-0-1 \n", + "12526 biomes human-gut-v2-0-1 \n", + "12527 biomes human-gut-v2-0-1 \n", + "12528 biomes human-gut-v2-0-1 \n", + "12529 biomes human-gut-v2-0-1 \n", + "\n", + " relationships.catalogue.data.type attributes.ena-study-accession \\\n", + "0 genome-catalogues None \n", + "1 genome-catalogues SRP308969 \n", + "2 genome-catalogues SRP308969 \n", + "3 genome-catalogues SRP308969 \n", + "4 genome-catalogues SRP308969 \n", + "... ... ... \n", + "12525 genome-catalogues ERP105624 \n", + "12526 genome-catalogues ERP105624 \n", + "12527 genome-catalogues ERP105624 \n", + "12528 genome-catalogues ERP105624 \n", + "12529 genome-catalogues ERP105624 \n", + "\n", + " attributes.pangenome-size attributes.pangenome-core-size \\\n", + "0 NaN NaN \n", + "1 1735.0 1688.0 \n", + "2 2230.0 1526.0 \n", + "3 2444.0 1872.0 \n", + "4 3216.0 1678.0 \n", + "... ... ... \n", + "12525 3622.0 3040.0 \n", + "12526 5274.0 920.0 \n", + "12527 14402.0 1163.0 \n", + "12528 28872.0 661.0 \n", + "12529 3154.0 1350.0 \n", + "\n", + " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \\\n", + "0 NaN None \n", + "1 47.0 None \n", + "2 704.0 None \n", + "3 572.0 None \n", + "4 1538.0 None \n", + "... ... ... \n", + "12525 582.0 None \n", + "12526 4354.0 None \n", + "12527 13239.0 None \n", + "12528 28211.0 None \n", + "12529 1804.0 None \n", + "\n", + " domain phylum class \\\n", + "0 d__Bacteria p__Bacillota c__Bacilli \n", + "1 d__Bacteria p__Pseudomonadota c__Alphaproteobacteria \n", + "2 d__Bacteria p__Actinomycetota c__Actinomycetia \n", + "3 d__Bacteria p__Pseudomonadota c__Gammaproteobacteria \n", + "4 d__Bacteria p__Pseudomonadota c__Gammaproteobacteria \n", + "... ... ... ... \n", + "12525 d__Bacteria p__Firmicutes_A c__Clostridia \n", + "12526 d__Bacteria p__Firmicutes_A c__Clostridia \n", + "12527 d__Bacteria p__Bacteroidota c__Bacteroidia \n", + "12528 d__Bacteria p__Firmicutes_A c__Clostridia \n", + "12529 d__Bacteria p__Firmicutes_A c__Clostridia \n", + "\n", + " order family genus \\\n", + "0 o__Lactobacillales f__Lactobacillaceae g__Lactobacillus \n", + "1 o__Acetobacterales f__Acetobacteraceae g__Bombella \n", + "2 o__Actinomycetales f__Bifidobacteriaceae g__Bifidobacterium \n", + "3 o__Enterobacterales f__Enterobacteriaceae g__Gilliamella \n", + "4 o__Enterobacterales f__Enterobacteriaceae g__Gilliamella \n", + "... ... ... ... \n", + "12525 o__Peptostreptococcales f__Peptostreptococcaceae g__Terrisporobacter \n", + "12526 o__Oscillospirales f__Ruminococcaceae g__Anaerotruncus \n", + "12527 o__Bacteroidales f__Rikenellaceae g__Alistipes \n", + "12528 o__Lachnospirales f__Lachnospiraceae g__Blautia_A \n", + "12529 o__Peptostreptococcales f__Peptostreptococcaceae g__GCA-900066495 \n", + "\n", + " species \n", + "0 s__Lactobacillus delbrueckii \n", + "1 s__ \n", + "2 s__ \n", + "3 s__Gilliamella sp945273075 \n", + "4 s__Gilliamella apicola_E \n", + "... ... \n", + "12525 s__Terrisporobacter glycolicus_A \n", + "12526 s__Anaerotruncus colihominis \n", + "12527 s__Alistipes shahii \n", + "12528 s__Blautia_A faecis \n", + "12529 s__GCA-900066495 sp902362365 \n", + "\n", + "[12530 rows x 44 columns]" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's see what the new columns look like (scroll to the right)\n", + "all_genomes_df" + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "id": "1c689272-26b9-41b1-9995-67714f661456", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "p__Firmicutes_A 4489\n", + "p__Bacteroidota 2176\n", + "p__Proteobacteria 1397\n", + "p__Firmicutes 1268\n", + "p__Actinobacteriota 1239\n", + "p__Firmicutes_C 287\n", + "p__Verrucomicrobiota 259\n", + "p__Cyanobacteria 237\n", + "p__Spirochaetota 136\n", + "p__Patescibacteria 119\n", + "p__Pseudomonadota 87\n", + "p__Campylobacterota 85\n", + "p__Chloroflexota 75\n", + "p__Fusobacteriota 73\n", + "p__Planctomycetota 58\n", + "p__Methanobacteriota 55\n", + "p__Desulfobacterota 48\n", + "p__Elusimicrobiota 44\n", + "p__Marinisomatota 43\n", + "p__Bacillota 40\n", + "Name: phylum, dtype: int64" + ] + }, + "execution_count": 348, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use the value_counts() to get the count of each phylum in the corresponding column.\n", + "# Sort values from largest to smallest by using method .sort_values(ascending=False) and keep just the first 20\n", + "# records (20 most common phyla). Store them in the phylum_counts series (one-dimensional array).\n", + "\n", + "phylum_counts = all_genomes_df['phylum'].value_counts().sort_values(ascending=False).head(20)\n", + "phylum_counts" + ] + }, + { + "cell_type": "markdown", + "id": "22ce77b9-79a5-4e7c-8c47-682bcdcfe15d", + "metadata": {}, + "source": [ + "We can add up all counts from phylum_counts to see what proportion of all genomes are from the top 20 phyla:" + ] + }, + { + "cell_type": "code", + "execution_count": 349, + "id": "0ac21473-cef7-497b-b9e0-2f4872e517cb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12215" + ] + }, + "execution_count": 349, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phylum_counts.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "f1e26003-8f6c-44f1-8229-4b87c8b37c02", + "metadata": {}, + "source": [ + "This tells us that out of a total of 12,530 genomes, 12,215 are in the 20 most abundant phyla." + ] + }, + { + "cell_type": "code", + "execution_count": 350, + "id": "ada2aec3-a17f-455f-b9af-4abdc9052c33", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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attributes.completenessattributes.contamination
count12530.00000012530.000000
mean89.5693881.025984
std10.6348151.096434
min50.0600000.000000
25%85.1200000.000000
50%93.0300000.740000
75%97.5500001.570000
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\n", + "
" + ], + "text/plain": [ + " attributes.completeness attributes.contamination\n", + "count 12530.000000 12530.000000\n", + "mean 89.569388 1.025984\n", + "std 10.634815 1.096434\n", + "min 50.060000 0.000000\n", + "25% 85.120000 0.000000\n", + "50% 93.030000 0.740000\n", + "75% 97.550000 1.570000\n", + "max 100.000000 5.000000" + ] + }, + "execution_count": 351, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_genomes_df[['attributes.completeness', 'attributes.contamination']].describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "011e2c7f-634b-4cb9-be7b-7f150936b84a", + "metadata": {}, + "source": [ + "and make a plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 353, + "id": "dc45345b-db52-4cf2-9e3b-6a920f9a6f26", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(5, 10), layout=\"constrained\") # set the size and layout\n", + "spec = fig.add_gridspec(1, 1) # set the grid\n", + "\n", + "ax00 = fig.add_subplot(spec[0, 0]) # create the placeholder for the boxplot\n", + "\n", + "# Create the boxplot using Seaborn's boxplot function. It visualizes the distribution \n", + "# of two columns from the DataFrame 'all_genomes_df': 'attributes.completeness' and 'attributes.contamination'. \n", + "# The boxplot shows the median, quartiles, and any outliers in the data for each column.\n", + "sns.boxplot(data=all_genomes_df[['attributes.completeness', 'attributes.contamination']])\n", + "\n", + "plt.ylabel(\"%\") # set the y-axis labes to show percentages\n", + "\n", + "\n", + "fig.suptitle('Quality of genomes in MGnify Genomes');" + ] + }, + { + "cell_type": "markdown", + "id": "c1cf839a-d222-4dfc-89ef-4b37a507640e", + "metadata": {}, + "source": [ + "---\n", + "## Bonus section: write your own code." + ] + }, + { + "cell_type": "markdown", + "id": "aa60ccc3-a856-4e33-9f4a-1db1ca496d7b", + "metadata": {}, + "source": [ + "The goal of this bonus section is to give you an opportunity to try and write code that access the MGnify API yourself. We will provide a template and there is a solution at the bottom if you need help." + ] + }, + { + "cell_type": "markdown", + "id": "74f885ae-b84b-4ca9-badb-2cc5ed53a089", + "metadata": {}, + "source": [ + "#### Task 1: query the API to find out how many genomes there are in catalogue." + ] + }, + { + "cell_type": "markdown", + "id": "d4a7f7c0-83a3-49b7-9ff7-497f7c652243", + "metadata": {}, + "source": [ + "First you need to find the API endpoint to use. Open [this link](https://www.ebi.ac.uk/metagenomics/api/v1/genome-catalogues) to look through the list of endpoints. You can click on them to see what information is inside. Once you have picked the endpoint, fill in the blank below:" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "id": "f587a45e-c800-4b5a-9580-5847632cb0c2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "endpoint = \"_________\" # fill in the blank" + ] + }, + { + "cell_type": "markdown", + "id": "d349a613-40e7-4457-991b-ead28583df33", + "metadata": {}, + "source": [ + "Write a codeblock below that will start an API session, iterate over the pages using the chosen endpoint and get JSONs for all catalogues. Convert JSONs into a dataframe called `resources_df`. Then print just the columns that contain catalogue name and the number of species in each (the species counts are saved in a variable called `attributes.genome-count` and there are several columns that contain catalogue name, you can pick any one.\n", + "\n", + "Hint: the code for quering the API is the same we already used to fetch analyses for a selected study earlier in the practical (but the endpoint is different)" + ] + }, + { + "cell_type": "code", + "execution_count": 364, + "id": "6cc2ebaa-d2a4-4a37-8e11-8708a28e1406", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name '______' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[364], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m______\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_____________________\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m mgnify:\n\u001b[1;32m 2\u001b[0m resources \u001b[38;5;241m=\u001b[39m __________________\n\u001b[1;32m 3\u001b[0m resources_df \u001b[38;5;241m=\u001b[39m __________________________\n", + "\u001b[0;31mNameError\u001b[0m: name '______' is not defined" + ] + } + ], + "source": [ + "with ______(\"_____________________\") as mgnify:\n", + " resources = __________________\n", + " resources_df = __________________________\n", + "resources_df[['_____________', 'attributes.genome-count']]" + ] + }, + { + "cell_type": "markdown", + "id": "be6607da-a45e-49ab-9366-aa3cd5bcdd3f", + "metadata": {}, + "source": [ + "Click on the three dots below to open the solution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dcb20f4-061d-411e-8622-1ad725191621", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Solution\n", + "endpoint = \"genome-catalogues\"\n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " resources = map(lambda r: r.json, mgnify.iterate(endpoint))\n", + " resources_df = pd.json_normalize(resources)\n", + " \n", + "# print the columns that contain the catalogue name and the number of genomes in each catalogue\n", + "resources_df[['attributes.name', 'attributes.genome-count']]" + ] + }, + { + "cell_type": "markdown", + "id": "f68b7fc4-742f-414e-ade7-8a6f0fe5c93c", + "metadata": {}, + "source": [ + "#### Task 2: generate a pie chart that would show relative sizes of the catalogues. Add catalogue names as labels on the chart." + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "id": "983ca9d7-1c11-4054-a7f6-ce745763e053", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name '_________________________' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[371], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Extract the 'attributes.name' and 'attributes.genome-count' columns from the resources_df dataframe\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m catalogue_names \u001b[38;5;241m=\u001b[39m \u001b[43m_________________________\u001b[49m\n\u001b[1;32m 3\u001b[0m genome_counts \u001b[38;5;241m=\u001b[39m __________________________\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Create the pie chart\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name '_________________________' is not defined" + ] + } + ], + "source": [ + "# Fill in the blanks in this code to generate the pie chart\n", + "\n", + "# Extract the 'attributes.name' and 'attributes.genome-count' columns from the resources_df dataframe\n", + "catalogue_names = _________________________\n", + "genome_counts = __________________________\n", + "\n", + "# Create the pie chart\n", + "plt.figure(figsize=(8, 8)) # Set the figure size\n", + "plt.pie(___________, labels=__________, autopct='%1.1f%%', startangle=140)\n", + "\n", + "# Set the title\n", + "plt.title('Relative Sizes of Catalogues')\n", + "\n", + "# Show the pie chart\n", + "____________" + ] + }, + { + "cell_type": "markdown", + "id": "21290ac4-2764-44cc-85d8-be54104cfb8b", + "metadata": {}, + "source": [ + "Click on the three dots below to see the solution." + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "id": "f2593f45-a17b-4342-94ad-5b5b439ff016", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Solution\n", + "# Extract the 'attributes.name' and 'attributes.genome-count' columns\n", + "catalogue_names = resources_df['attributes.name']\n", + "genome_counts = resources_df['attributes.genome-count']\n", + "\n", + "# Create the pie chart\n", + "plt.figure(figsize=(8, 8)) # Set the figure size\n", + "plt.pie(genome_counts, labels=catalogue_names, autopct='%1.1f%%', startangle=140)\n", + "\n", + "# Set the title\n", + "plt.title('Relative Sizes of Catalogues')\n", + "\n", + "# Show the pie chart\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0b3adcca-9c2f-4a5b-91d7-10d34d294df7", + "metadata": {}, + "source": [ + "#### Task 3: download a genome from the API" + ] + }, + { + "cell_type": "markdown", + "id": "2f962ee7-2845-460c-a3f3-1e7372ed4ac5", + "metadata": {}, + "source": [ + "We have downloaded analysis results earlier in this practical (at the end of Section 1). You can download genomes and annotations in a similar way. \n", + "For this exercise, let's download all predicted CDS for genome `MGYG000321626`.\n", + "\n", + "Start by identifying the endpoint you would need for this [here](https://www.ebi.ac.uk/metagenomics/api/v1/). \n", + "\n", + "Hint: review what endpoints looked like when we downloaded analyses - they are not always just one level beyond the base URL." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f1f8111-4174-44c2-a795-ea9a7b1ab63f", + "metadata": {}, + "outputs": [], + "source": [ + "endpoint = \"______________\"" + ] + }, + { + "cell_type": "markdown", + "id": "1113c919-741c-4aad-a487-402b9d344bac", + "metadata": {}, + "source": [ + "Fill in the blanks in the code block below to download the file. To see the file format and description label that we are looking for, use the link to API, navigate to the appropriate endpoint and see what the results look like in the browser." + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "id": "16381adf-54d1-4c6f-a11b-6cf6035a30c5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: MGYA00646965\n", + "Downloading file for MGYA00646965\n", + "Finished for MGYA00646965\n", + "Processing: MGYA00646967\n", + "Downloading file for MGYA00646967\n", + "Finished for MGYA00646967\n" + ] + } + ], + "source": [ + "endpoint = \"___________________\" \n", + "\n", + "with APISession(\"__________________\") as session: \n", + " for download in session.iterate(endpoint):\n", + " # Go over the different files to download until we find the one with CDS results\n", + " cds_file = \"MGYG000321626.faa\"\n", + " if (\n", + " download.description.label =='_____________'\n", + " and\n", + " download.file_format.name == '_____________'\n", + " ):\n", + " print(f\"Downloading the file\")\n", + " urlretrieve(_____________________________)\n", + " break\n", + "print(\"Finished downloading\", cds_file)" + ] + }, + { + "cell_type": "markdown", + "id": "583aef6a-726a-4855-b14b-1d3b481bf920", + "metadata": {}, + "source": [ + "Click on the three dots below to see the solution." + ] + }, + { + "cell_type": "code", + "execution_count": 378, + "id": "8f5821ed-268a-4030-a2a5-e5d709e28b56", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading the file\n", + "Finished downloading MGYG000321626.faa\n" + ] + } + ], + "source": [ + "endpoint = \"genomes/MGYG000321626/downloads\" # we are only interested in a specific genome\n", + "\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session: \n", + " for download in session.iterate(endpoint):\n", + " # Go over the different files to download until we find the one with CDS results\n", + " cds_file = \"MGYG000321626.faa\"\n", + " if (\n", + " download.description.label =='Predicted CDS (aa)'\n", + " and\n", + " download.file_format.name == 'FASTA'\n", + " ):\n", + " print(f\"Downloading the file\")\n", + " urlretrieve(download.links.self.url, cds_file)\n", + " break\n", + "print(\"Finished downloading\", cds_file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (mgnify-py-env)", + "language": "python", + "name": "conda-env-mgnify-py-env-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git 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z1H_-*QY?h_Bt9cR)$od*VR&g>&N_&dzam z&vWpX+RY8;z)Rd;@Yn=m=fAQ3;4^MSeUpy{Z^fa=pKf?>{Aqt32VJ+>5>Zv#FZJ=t zHyP-9k!YIn%Do${F>wA5o@CuBZ=B6Fo`iA`|CQb=AEd;K@flQx51MOvfzC6;({aEb z4n#=bloQEYHt`l_UnK7*T|d968y7)7#k2C!v_`(RJtNw43+m(RUk8WDR^$CaM7Q&E z%m14Bkv)TGAI(iOfQUFk;cgP{^O1Ne5(O^#D4&Kc{B*at*p%q?D1oyK@3Wl=R~hb= zPaAlA$nc%+r*g!VTTqeco30Xg68;tV4T+35_4S#bjJ|$fdp@ba5071Muhd-*l;6Vq zihj}jG98-v@O#rwlO-7Natu+Je@5;stCSyozG&!2Xrmu(H7>3p6NVs@*RP*<-{$YO z_{Dio59`4r>|^S&8LpT4Jf&@oeD>_go!`}XRgLHKfi#&#c!L%^ zS;ysrA@%Fl?|XZVtvoIE+B-vpwCry~ErP#trHFr|Zm9=6zexU%;@@%1%;)06W>Pq} z$~2zTU+=@c`hUY-ZT`Q1jtu`8s65I~i{f(|?<$J>3-2LHlOGF#Ye0k@mRPw{I6d3t)+03zS12_Ype~#73w$L1RUt`eLXICkw41vqAxhl zl;;T)C3sVGrE) Date: Thu, 3 Aug 2023 15:21:04 +0100 Subject: [PATCH 2/4] Fixes --- .../GSC23 MGnify Workshop.ipynb | 4756 ++++------------- 1 file changed, 962 insertions(+), 3794 deletions(-) diff --git a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb index eaeb758..9e5f424 100644 --- a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb +++ b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 354, + "execution_count": 1, "id": "ba4e90f6-7778-4d03-ae4f-05711d5d5c63", "metadata": { "tags": [] @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 2, "id": "80005880-ec79-4991-96b0-b1d8848eac99", "metadata": { "tags": [] @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": null, "id": "0941782b-69e3-4c48-b036-fca19d9dde40", "metadata": { "jupyter": { @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 3, "id": "d1661765-bdf8-44b5-9c10-db0ffe7fb4d5", "metadata": { "tags": [] @@ -256,12 +256,12 @@ "source": [ "#### Fetch data for the selected study\n", "\n", - "Load analyses from this study into a [Pandas dataframe](https://pandas.pydata.org/docs/user_guide/index.html). Pandas is a Python library that is widely used to work with and manipulate data. A dataframe is a table with rows and columns. Note that we will be using a different endpoint here because now we are fetching analyses rather than studies." + "Load analyses from this study into a [Pandas dataframe](https://pandas.pydata.org/docs/user_guide/index.html). Pandas is a Python library that is widely used to work with and manipulate data. A dataframe is a table with rows and columns. Note that we will be using a different endpoint here because now we are fetching analyses rather than studies. You will also notice that endpoints can go several levels beyond the original URL. In this example it's original URL + `studies` + \"specific study accession\" + `analyses`" ] }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 4, "id": "5173ced5-e607-49a2-88d1-88c57d68baaf", "metadata": { "tags": [] @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 5, "id": "f1071967-42f7-4890-99dd-58d138ebffac", "metadata": { "tags": [] @@ -318,9 +318,9 @@ " id\n", " attributes.accession\n", " attributes.analysis-status\n", - " attributes.pipeline-version\n", - " attributes.analysis-summary\n", " attributes.experiment-type\n", + " attributes.analysis-summary\n", + " attributes.pipeline-version\n", " attributes.is-private\n", " attributes.complete-time\n", " attributes.instrument-platform\n", @@ -337,101 +337,101 @@ " \n", " 0\n", " analysis-jobs\n", - " MGYA00646965\n", - " MGYA00646965\n", + " MGYA00585242\n", + " MGYA00585242\n", " completed\n", - " 5.0\n", - " [{'key': 'Submitted nucleotide sequences', 'value': '16279'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '11515'}, {'key': 'Nucleotide sequences after length filtering', 'value': '11515'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '11515'}, {'key': 'Predicted SSU sequences', 'value': '10046'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", " amplicon\n", + " [{'key': 'Submitted nucleotide sequences', 'value': '1474912'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after length filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '335618'}, {'key': 'Predicted SSU sequences', 'value': '335119'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", + " 5.0\n", " False\n", - " 2023-07-19T14:11:39\n", - " LS454\n", - " 454 GS FLX\n", - " ERS161286\n", + " 2021-07-23T09:20:39\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871389\n", " samples\n", - " ERR162887\n", + " ERR2098577\n", " runs\n", - " MGYS00006253\n", + " MGYS00001935\n", " studies\n", " \n", " \n", " 1\n", " analysis-jobs\n", - " MGYA00646967\n", - " MGYA00646967\n", + " MGYA00585243\n", + " MGYA00585243\n", " completed\n", - " 5.0\n", - " [{'key': 'Submitted nucleotide sequences', 'value': '9967'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after length filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '9113'}, {'key': 'Predicted SSU sequences', 'value': '8431'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", " amplicon\n", + " [{'key': 'Submitted nucleotide sequences', 'value': '1178982'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1060738'}, {'key': 'Predicted SSU sequences', 'value': '1049588'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", + " 5.0\n", " False\n", - " 2023-07-19T14:11:59\n", - " LS454\n", - " 454 GS FLX\n", - " ERS161300\n", + " 2021-07-23T09:22:00\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871409\n", " samples\n", - " ERR162901\n", + " ERR2098437\n", " runs\n", - " MGYS00006253\n", + " MGYS00001935\n", " studies\n", " \n", " \n", " 2\n", " analysis-jobs\n", - " MGYA00646970\n", - " MGYA00646970\n", + " MGYA00585244\n", + " MGYA00585244\n", " completed\n", - " 5.0\n", - " [{'key': 'Submitted nucleotide sequences', 'value': '8658'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7850'}, {'key': 'Predicted SSU sequences', 'value': '7057'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", " amplicon\n", + " [{'key': 'Submitted nucleotide sequences', 'value': '2285192'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after length filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '772970'}, {'key': 'Predicted SSU sequences', 'value': '772662'}, {'key': 'Predicted LSU sequences', 'value': '3'}]\n", + " 5.0\n", " False\n", - " 2023-07-19T14:12:24\n", - " LS454\n", - " 454 GS FLX\n", - " ERS161297\n", + " 2021-07-23T09:24:06\n", + " ILLUMINA\n", + " Illumina MiSeq\n", + " ERS1871427\n", " samples\n", - " ERR162898\n", + " ERR2098500\n", " runs\n", - " MGYS00006253\n", + " MGYS00001935\n", " studies\n", " \n", " \n", " 3\n", " analysis-jobs\n", - " MGYA00646973\n", - " MGYA00646973\n", + " MGYA00585245\n", + " MGYA00585245\n", " completed\n", - " 5.0\n", - " [{'key': 'Submitted nucleotide sequences', 'value': '7008'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after length filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '6632'}, {'key': 'Predicted SSU sequences', 'value': '6556'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", " amplicon\n", + " [{'key': 'Submitted nucleotide sequences', 'value': '1863283'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1679669'}, {'key': 'Predicted SSU sequences', 'value': '1674242'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", + " 5.0\n", " False\n", - " 2023-07-19T14:12:50\n", - " LS454\n", - " 454 GS FLX\n", - " ERS161295\n", + " 2021-07-23T09:25:40\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871411\n", " samples\n", - " ERR162896\n", + " ERR2098439\n", " runs\n", - " MGYS00006253\n", + " MGYS00001935\n", " studies\n", " \n", " \n", " 4\n", " analysis-jobs\n", - " MGYA00646976\n", - " MGYA00646976\n", + " MGYA00585246\n", + " MGYA00585246\n", " completed\n", - " 5.0\n", - " [{'key': 'Submitted nucleotide sequences', 'value': '8317'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7225'}, {'key': 'Predicted SSU sequences', 'value': '6692'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", " amplicon\n", + " [{'key': 'Submitted nucleotide sequences', 'value': '1244024'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1110539'}, {'key': 'Predicted SSU sequences', 'value': '1107011'}, {'key': 'Predicted LSU sequences', 'value': '0'}]\n", + " 5.0\n", " False\n", - " 2023-07-19T14:13:10\n", - " LS454\n", - " 454 GS FLX\n", - " ERS161294\n", + " 2021-07-23T09:27:22\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871430\n", " samples\n", - " ERR162895\n", + " ERR2098450\n", " runs\n", - " MGYS00006253\n", + " MGYS00001935\n", " studies\n", " \n", " \n", @@ -440,63 +440,63 @@ ], "text/plain": [ " type id attributes.accession \\\n", - "0 analysis-jobs MGYA00646965 MGYA00646965 \n", - "1 analysis-jobs MGYA00646967 MGYA00646967 \n", - "2 analysis-jobs MGYA00646970 MGYA00646970 \n", - "3 analysis-jobs MGYA00646973 MGYA00646973 \n", - "4 analysis-jobs MGYA00646976 MGYA00646976 \n", - "\n", - " attributes.analysis-status attributes.pipeline-version \\\n", - "0 completed 5.0 \n", - "1 completed 5.0 \n", - "2 completed 5.0 \n", - "3 completed 5.0 \n", - "4 completed 5.0 \n", - "\n", - " attributes.analysis-summary \\\n", - "0 [{'key': 'Submitted nucleotide sequences', 'value': '16279'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '11515'}, {'key': 'Nucleotide sequences after length filtering', 'value': '11515'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '11515'}, {'key': 'Predicted SSU sequences', 'value': '10046'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "1 [{'key': 'Submitted nucleotide sequences', 'value': '9967'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after length filtering', 'value': '9113'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '9113'}, {'key': 'Predicted SSU sequences', 'value': '8431'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "2 [{'key': 'Submitted nucleotide sequences', 'value': '8658'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7850'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7850'}, {'key': 'Predicted SSU sequences', 'value': '7057'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "3 [{'key': 'Submitted nucleotide sequences', 'value': '7008'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after length filtering', 'value': '6632'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '6632'}, {'key': 'Predicted SSU sequences', 'value': '6556'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "4 [{'key': 'Submitted nucleotide sequences', 'value': '8317'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after length filtering', 'value': '7225'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '7225'}, {'key': 'Predicted SSU sequences', 'value': '6692'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "\n", - " attributes.experiment-type attributes.is-private attributes.complete-time \\\n", - "0 amplicon False 2023-07-19T14:11:39 \n", - "1 amplicon False 2023-07-19T14:11:59 \n", - "2 amplicon False 2023-07-19T14:12:24 \n", - "3 amplicon False 2023-07-19T14:12:50 \n", - "4 amplicon False 2023-07-19T14:13:10 \n", + "0 analysis-jobs MGYA00585242 MGYA00585242 \n", + "1 analysis-jobs MGYA00585243 MGYA00585243 \n", + "2 analysis-jobs MGYA00585244 MGYA00585244 \n", + "3 analysis-jobs MGYA00585245 MGYA00585245 \n", + "4 analysis-jobs MGYA00585246 MGYA00585246 \n", + "\n", + " attributes.analysis-status attributes.experiment-type \\\n", + "0 completed amplicon \n", + "1 completed amplicon \n", + "2 completed amplicon \n", + "3 completed amplicon \n", + "4 completed amplicon \n", + "\n", + " attributes.analysis-summary \\\n", + "0 [{'key': 'Submitted nucleotide sequences', 'value': '1474912'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after length filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '335618'}, {'key': 'Predicted SSU sequences', 'value': '335119'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "1 [{'key': 'Submitted nucleotide sequences', 'value': '1178982'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1060738'}, {'key': 'Predicted SSU sequences', 'value': '1049588'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "2 [{'key': 'Submitted nucleotide sequences', 'value': '2285192'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after length filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '772970'}, {'key': 'Predicted SSU sequences', 'value': '772662'}, {'key': 'Predicted LSU sequences', 'value': '3'}] \n", + "3 [{'key': 'Submitted nucleotide sequences', 'value': '1863283'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1679669'}, {'key': 'Predicted SSU sequences', 'value': '1674242'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "4 [{'key': 'Submitted nucleotide sequences', 'value': '1244024'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1110539'}, {'key': 'Predicted SSU sequences', 'value': '1107011'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", + "\n", + " attributes.pipeline-version attributes.is-private attributes.complete-time \\\n", + "0 5.0 False 2021-07-23T09:20:39 \n", + "1 5.0 False 2021-07-23T09:22:00 \n", + "2 5.0 False 2021-07-23T09:24:06 \n", + "3 5.0 False 2021-07-23T09:25:40 \n", + "4 5.0 False 2021-07-23T09:27:22 \n", "\n", " attributes.instrument-platform attributes.instrument-model \\\n", - "0 LS454 454 GS FLX \n", - "1 LS454 454 GS FLX \n", - "2 LS454 454 GS FLX \n", - "3 LS454 454 GS FLX \n", - "4 LS454 454 GS FLX \n", + "0 ILLUMINA Illumina HiSeq 2500 \n", + "1 ILLUMINA Illumina HiSeq 2500 \n", + "2 ILLUMINA Illumina MiSeq \n", + "3 ILLUMINA Illumina HiSeq 2500 \n", + "4 ILLUMINA Illumina HiSeq 2500 \n", "\n", " relationships.sample.data.id relationships.sample.data.type \\\n", - "0 ERS161286 samples \n", - "1 ERS161300 samples \n", - "2 ERS161297 samples \n", - "3 ERS161295 samples \n", - "4 ERS161294 samples \n", + "0 ERS1871389 samples \n", + "1 ERS1871409 samples \n", + "2 ERS1871427 samples \n", + "3 ERS1871411 samples \n", + "4 ERS1871430 samples \n", "\n", " relationships.run.data.id relationships.run.data.type \\\n", - "0 ERR162887 runs \n", - "1 ERR162901 runs \n", - "2 ERR162898 runs \n", - "3 ERR162896 runs \n", - "4 ERR162895 runs \n", + "0 ERR2098577 runs \n", + "1 ERR2098437 runs \n", + "2 ERR2098500 runs \n", + "3 ERR2098439 runs \n", + "4 ERR2098450 runs \n", "\n", " relationships.study.data.id relationships.study.data.type \n", - "0 MGYS00006253 studies \n", - "1 MGYS00006253 studies \n", - "2 MGYS00006253 studies \n", - "3 MGYS00006253 studies \n", - "4 MGYS00006253 studies " + "0 MGYS00001935 studies \n", + "1 MGYS00001935 studies \n", + "2 MGYS00001935 studies \n", + "3 MGYS00001935 studies \n", + "4 MGYS00001935 studies " ] }, - "execution_count": 225, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 6, "id": "fd8ef42e-ea80-4a55-863e-12342dddab5a", "metadata": { "tags": [] @@ -531,7 +531,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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o0qULhBAGx2+HDh2Qmpqq/9s5Ozvj1q1b+Ouvv576eIvSr18/ODg46H/v2bMnfHx89Md3/jpHjx41OKW5atUqBAQEoGXLls+037Zt26Jy5cr632vXrg1HR0eD56+446ok+vfvb/D3fhbPegwkJydj79696NWrl/49LSkpCffv30eHDh0QGxurPzWQ76233jI4pdK8eXNotVpcv379uR5DYfr164c7d+4YvJevWrUKarUaPXr0MFhXqVTi7bff1v9ubW2Nt99+G4mJiTh27BiAkr/nVjQVKjx8/fXX+O2337Bv3z6cO3dOf84UyDunK4RAaGgoPDw8DH7Onz+PxMREg235+fkV6Kh35coVhIWFFdmRKTY2FqmpqfD09Cywn/T09AL7efxFlM/FxaVA/4inOXv2LF5++WU4OTnB0dERHh4e+g+o/Df0/BdQlSpVDO6rVCoLjN6IjY3F2bNnC9RetWpVANDX/+qrr6Jp06YYPHgwvLy80Lt3b/z000/FBonr169DLpcXqMXb2xvOzs7P/GK/ceMGBgwYAFdXV30/hvw36Cc/2FQqlf6cZr7CnvMtW7agUaNGUKlUcHV1hYeHBxYuXFhge09ydnZGly5dDM6fr1q1Cn5+fvo3BAD48ssvcebMGQQEBKBhw4aYMGGCwYdAbGwsgLw38if/HsuWLUN2djZSU1Nx7949PHr0CKGhoQVqCQsLK7JWAAgODsbIkSOxbNkyuLu7o0OHDvj6668LPM7Dhw+jbdu2sLOzg7OzMzw8PPR9XJ5c98njOj+gBAQEFLr8yedeLpcjJCTEYFn+Mfi0Icv37t1DSkoKlixZUuD5yv8ikH/8jhkzBvb29mjYsCFCQ0Px3nvvFdqP5GmefK5lMhmqVKliUNurr74KGxsbfYhMTU3Fli1b0Ldv32eew6Ek7xfFHVclERwc/Ez1FVVrSY+By5cvQwiB8ePHF/g7fvbZZwBQ7Pto/peikr6PGqNdu3bw8fHR/111Oh1+/PFHREdHGwRKAPD19YWdnZ3BsieP45K+51Y0ptHdt4QaNmyoH23xJJ1OB5lMhu3bt0OhUBS43d7e3uD3Z03dOp0Onp6eBt86H/fkh1ZhtQAoUcewlJQUtGzZEo6Ojpg0aRIqV64MlUqF48ePY8yYMUa1CDxef61atTBz5sxCb89/4avVahw8eBD79u3D1q1bsWPHDqxduxatW7fGrl27nvq48pXmBDharRbt2rVDcnIyxowZg/DwcNjZ2eH27dsYMGBAgeehuNqAvA6YXbt2RYsWLbBgwQL4+PjAysoKK1asKFGnun79+mHdunWIiYlBrVq18Msvv2Do0KEGPdZ79eqF5s2bY9OmTdi1axe++uorTJ8+HRs3bsSLL76or/urr7566gRZ9vb2pdKpasaMGRgwYAB+/vln7Nq1C++//z6++OIL/PHHH/D398eVK1fQpk0bhIeHY+bMmQgICIC1tTW2bduGWbNmlfg5fp7jvTj5Nbz++uvo379/oevUrl0bQF6rzMWLF7Flyxbs2LEDGzZswIIFC/Dpp5/qh+g9LxcXF7z00ktYtWoVPv30U6xfvx7Z2dkGrU/GKsnzV9xxVRKFvf897TVbWOfsomot7jHk/x1HjRr11JazJ798lOVx9SSFQoE+ffpg6dKlWLBgAQ4fPow7d+4889+1pO+5FU2FCg9FqVy5MoQQCA4O1ie6Z9nG0aNHkZubCysrq6eus3v3bjRt2vS5m/3yPe1Fu3//fty/fx8bN25EixYt9MsfH10CAIGBgQDyEv0LL7ygX67RaBAXF6d/Q82v/9SpU2jTpk2xH/ByuRxt2rRBmzZtMHPmTEydOhVjx47Fvn370LZt20LvExgYCJ1Oh9jYWFSrVk2//O7du0hJSdHXaozTp0/j0qVL+Pbbb9GvXz/98t9++83obeXbsGEDVCoVdu7cCRsbG/3yFStWlOj+HTt2hIeHB1atWoWoqChkZmbijTfeKLCej48Phg4diqFDhyIxMRH16tXD559/jhdffFHfPO3o6PjU5xPIC6RqtVrfUvG4ixcvlqheAKhVqxZq1aqFcePGISYmBk2bNsWiRYswZcoU/Prrr8jOzsYvv/xi8C2vrJpUdTodrl69avBavXTpEgA8da4TDw8PODg4QKvVFvl85bOzs8Orr76KV199FTk5OejevTs+//xzfPzxx1CpVEXe98nnWgiBy5cvG7yWgLwQGR0djb/++gurVq1C3bp1UaNGjWJre15FHVfAs4X3/G/zKSkp+tOWAEr91EB+i5OVlVWJ/o4lZcxjLm7dfv36YcaMGfj111+xfft2eHh4FBp07ty5g4yMDIPWhyePY2PecyuSCnXaoijdu3eHQqHAxIkTC6RRIQTu379f7DZ69OiBpKQkzJ8/v8Bt+dvs1asXtFotJk+eXGAdjUaDlJQUo2vPP/CevG9+2n788eTk5GDBggUG69WvXx9ubm5YunQpNBqNfvmqVasKNOv16tULt2/fxtKlSwvU8ejRI2RkZADIOy/5pPxvx0V9E+7UqRMAFJjdLj91d+7c+an3fZrCngchBObMmWP0th7fpkwmM/hWFRcXh82bN5fo/kqlEq+99hp++uknrFy5ErVq1TL4YNFqtQWa+j09PeHr66t//iIjI1G5cmX83//9H9LT0wvsI38omkKhQIcOHbB582bcuHFDf/v58+exc+fOYmtNS0szOC6AvCAhl8v1tRT2HKemppY4TD2Lx19nQgjMnz8fVlZWaNOmTaHrKxQK9OjRAxs2bMCZM2cK3P740L0nX+/W1taoXr06hBDIzc0ttrbvvvsODx8+1P++fv16xMfHF/hm/+KLL8Ld3R3Tp0/HgQMHnqvVoSRKclwBee8pxZ1+e1J+mD148KB+WUZGBr799tvnqLggT09PtGrVCosXL0Z8fHyB2wsb/loSdnZ2JX7/zZ/T52nr165dG7Vr18ayZcuwYcMG9O7du9DT2RqNxmDIdE5ODhYvXgwPDw9ERkYCKPl7bkVjVi0PU6ZMwccff4y4uDh069YNDg4OuHbtGjZt2oS33noLo0aNKnIb/fr1w3fffYeRI0fizz//RPPmzZGRkYHdu3dj6NChiI6ORsuWLfH222/jiy++wMmTJ9G+fXtYWVkhNjYW69atw5w5c9CzZ0+jao+IiIBCocD06dORmpoKGxsbtG7dGk2aNIGLiwv69++P999/HzKZDN9//32BcGRtbY0JEyZg+PDhaN26NXr16oW4uDisXLkSlStXNki7b7zxBn766Se888472LdvH5o2bQqtVosLFy7gp59+ws6dO1G/fn1MmjQJBw8eROfOnREYGIjExEQsWLAA/v7+aNas2VMfS506ddC/f38sWbJEf9rlzz//xLfffotu3boZtIyUVHh4OCpXroxRo0bh9u3bcHR0xIYNG57rfGfnzp0xc+ZMdOzYEX369EFiYiK+/vprVKlSBf/880+JttGvXz/MnTsX+/btw/Tp0w1ue/jwIfz9/dGzZ0/UqVMH9vb22L17N/766y/MmDEDQF7LzrJly/Diiy+iRo0aePPNN+Hn54fbt29j3759cHR0xK+//gogbzz6jh070Lx5cwwdOhQajUY/j0Fx9e7duxfDhg3DK6+8gqpVq0Kj0eD777/XfxgDQPv27WFtbY0uXbrg7bffRnp6OpYuXQpPT89C3+Cfl0qlwo4dO9C/f39ERUVh+/bt2Lp1Kz755JMCp/4eN23aNOzbtw9RUVEYMmQIqlevjuTkZBw/fhy7d+/Wh9727dvD29sbTZs2hZeXF86fP4/58+ejc+fOBc5bF8bV1RXNmjXDm2++ibt372L27NmoUqUKhgwZYrCelZUVevfujfnz50OhUDz3ZHXFKclxBeQF07Vr12LkyJFo0KAB7O3t0aVLlyK33b59e1SqVAmDBg3CRx99BIVCgeXLl8PDw8MgtJaGr7/+Gs2aNUOtWrUwZMgQhISE4O7duzhy5Ahu3bqFU6dOGb3NyMhILFy4EFOmTEGVKlXg6elp0AfpcWq1GtWrV8fatWtRtWpVuLq6ombNmqhZs6Z+nX79+uk/M54WCn19fTF9+nTExcWhatWqWLt2LU6ePIklS5boW69L+p5b4ZTn0I5n9bR5HgqzYcMG0axZM2FnZyfs7OxEeHi4eO+998TFixf167Rs2fKpw7gyMzPF2LFjRXBwsLCyshLe3t6iZ8+eBeYgWLJkiYiMjBRqtVo4ODiIWrVqidGjR4s7d+7o1wkMDBSdO3cusI+WLVsWGFK0dOlSERISoh96lz+s6fDhw6JRo0ZCrVYLX19fMXr0aP2QqCeHg82dO1cEBgYKGxsb0bBhQ3H48GERGRkpOnbsaLBeTk6OmD59uqhRo4awsbERLi4uIjIyUkycOFGkpqYKIfLmH4iOjha+vr7C2tpa+Pr6itdee01cunSpyOdfCCFyc3PFxIkT9c9hQECA+Pjjjw2GZQlh3FDNc+fOibZt2wp7e3vh7u4uhgwZoh/G9vjwqadts7BhaN98840IDQ0VNjY2Ijw8XKxYsaLQ9Z4cqvm4GjVqCLlcLm7dumWwPDs7W3z00UeiTp06wsHBQdjZ2Yk6deoUmGtCiLyhlN27dxdubm7CxsZGBAYGil69eok9e/YYrHfgwAERGRkprK2tRUhIiFi0aNFTh9c97urVq2LgwIGicuXKQqVSCVdXV/HCCy+I3bt3G6z3yy+/iNq1awuVSiWCgoLE9OnT9cOXnxymV9hxDaDAEMJr164JAOKrr77SL8v/G125ckU//t3Ly0t89tlnBsPj8rf55HC6u3fvivfee08EBAToX6Nt2rQRS5Ys0a+zePFi0aJFC/1zWrlyZfHRRx/pj++nyR/K+OOPP4qPP/5YeHp6CrVaLTp37mww/PBxf/75pwAg2rdvX+S2H/e0oZqFDcF8/Pgr6XGVnp4u+vTpI5ydnQUA/bDN/Mf3tGG7x44dE1FRUcLa2lpUqlRJzJw586lDNZ/nGBAib46bfv36CW9vb2FlZSX8/PzESy+9JNavX69f52nv/fmP4/H3wISEBNG5c2fh4OBQYIh6YWJiYvSvp8KOs/j4eKFQKETVqlULvX/+58jff/8tGjduLFQqlQgMDCwwL4oQJXvPrWhkQpRBjxMyCTqdDh4eHujevXuhTWb0/OrWrQtXV1fs2bNH6lIqjAEDBmD9+vWFnqqpiE6dOoWIiAh89913hfZ7oYopKSkJPj4++PTTTzF+/PgCt7dq1QpJSUmFnkKzBGbT58HSZWVlFTid8d133yE5OZkXfikjf//9N06ePGnQiZMsz9KlS2Fvb4/u3btLXQqVopUrV0Kr1TIQPoXZ9HmwdH/88Qc++OADvPLKK3Bzc8Px48fxzTffoGbNmvprZ1DpOHPmDI4dO4YZM2bAx8cHr776qtQlkQR+/fVXnDt3DkuWLMGwYcMKjPenimnv3r04d+4cPv/8c3Tr1s2irnRsDIYHMxEUFISAgADMnTsXycnJcHV1Rb9+/TBt2jRetbKUrV+/HpMmTUJYWBh+/PHHYof9kXkaPnw47t69i06dOpXa3BEkvUmTJumHMs+bN0/qckwW+zwQERGRUdjngYiIiIzC8EBERERGYXggIiIiozA8EBERkVEYHoiIiMgoDA9ERERkFIYHIiIiMgrDAxERERmF4YGIiIiMwvBARERERmF4ICIiIqMwPBAREZFRGB6IiIjIKAwPREREZBSGByIiIjIKwwMREREZheGBiIiIjMLwQEREREZheCAiIiKjMDwQERGRURgeiIiIyCgMD0RERGQUhgciIiIyCsMDERERGYXhgYiIiIzC8EBERERGYXggIiIiozA8EBERkVEYHoiIiMgoDA9ERERkFIYHIiIiMgrDAxERERmF4YGIiIiMwvBARERERmF4ICIiIqMwPBAREZFRGB6IiIjIKAwPREREZBSGByIiIjKKUuoCiKjsJGfkIPFhFu6mZeN+ejayNTpk52qRo9UhR6NDtuaxf7U6ZOfm/SsDYGutgNpaATtr5f/+VcA2//82CqitlLCzUcDDwQZeDirI5TKpHy4RlROGB6IKKvFhFq4kZuBOyiPcfZiFxLRs3E3L+t9PNu6lZyNHoyuXWpRyGbwcVfBzVsPXWQVfZzV8ndXwc1bDzyXv//Y2fLshMhcyIYSQuggierqUzBycu5OG8wkPEXv3IS4npuPyvXSkZOZKXZpRXGytUM3HETX9nFDD1xE1fB0R4m7PFguiCojhgciEZGRr8GdcMk7cSMG5O6k4dycNd1KzpC6rzKitFAj3cfhfmMgLFWHeDrBRKqQujYiKwPBAJKHMHA3+inuAI1fu44+r93Hmdio0Ost+SSrlMtT0c0LzUHc0reKOepVcYK1k324iU8LwQFSOHuVo8ff1ZH1Y+OcWw0JxbK0VaBjsimZV3NE81ANh3g5Sl0Rk8RgeiMpYQmoWtp+Jx/YzCThx4wFytXzJPQ8PBxs0q5LXKtGiqjs8HVRSl0RkcRgeiMpAfOojbDudgG2n43H8xgPwVVY25DIgKtgNXSN80ammD5xsraQuicgiMDwQlZLbKY+w/XQ8tp2Ox4mbKQwM5cxaIUeLqu7oUscX7ap7wdaaQ0OJygrDA9FzSM7Iwcbjt7Dln3icusXAYCrUVgq0re6FrnV80bKqBztcEpUyhgeiZ3DsejJ++OMGtp6OL7eJmOjZOKmt0KmWN/pGBaKmn5PU5RCZBYYHohLKzNFg84k7+OGP6zgXnyZ1OfQM6lZyRr/GgehUy4dzSRA9B4YHomJcTkzHD39cx4bjt/AwSyN1OVQK3Oys0bthAPo3DoKnI0drEBmL4YGoEEII7Dx7F9/GxOHI1ftSl0NlxFohx0u1fTC4eQiq+zpKXQ5RhcHwQPQYIQS2nU7A3D2xuHj3odTlUDlqHOKGt1uGoFWYp9SlEJk8hgci5IWG7WcSMGc3Q4Olqx/ogo86hCEqxE3qUohMFsMDWbT80DB3TywuJDA00L+ah7rjow5hqO3vLHUpRCaH4YEskhACO84kYA5DAxWjQw0vfNg+DFW9eE0NonwMD2Rx9l9MxPQdF3Gewy2phOQyIDrCDyPahiLQzU7qcogkx/BAFuNmciYm/noOu8/flboUqqCUchl6NQjAB22rwsPBRupyiCTD8EBmLytXi0UHrmDh/ivI5myQVAocVUqM7hiOvlGVIJPJpC6HqNwxPJBZ++3cXUzachY3kx9JXQqZobqVnPF5t1qcI4IsDsMDmaW4pAxM/PUs9l28J3UpZOYUchkGNAnCyHZVYWfDK3mSZWB4ILPyKEeLr/ddxpJDV3nBKipXPk4qfNalBjrW9Ja6FKIy91zXqW3VqhVGjBih/z0oKAizZ89+zpKM36/UTK0eS3X8xgO8OOcg5u+7zOBA5S4+NQvv/HAMg7/9C7ceZEpdDlGZMio8DBgwAN26dSujUkpu48aNmDx5cpnvRyaTYfPmzQWWP/k8PFnPtWvX0KdPH/j6+kKlUsHf3x/R0dG4cOFCmda7f/9+REdHw8fHB3Z2doiIiMCqVasM1lm5ciVkMpnBj0pleGEgIQQ+/fRT+Pj4QK1Wo23btoiNjTVYJzk5GX379oWjoyOcnZ0xaNAgpKenl+njexqNVoeZuy7ilUVHEHefb9okrd3nE9Fu5kF8GxMndSlEZea5Wh6k4urqCgcH05mw5fF6cnNz0a5dO6SmpmLjxo24ePEi1q5di1q1aiElJaVM64iJiUHt2rWxYcMG/PPPP3jzzTfRr18/bNmyxWA9R0dHxMfH63+uX79ucPuXX36JuXPnYtGiRTh69Cjs7OzQoUMHZGVl6dfp27cvzp49i99++w1btmzBwYMH8dZbb5Xp4yvM1Xvp6LEwBnP3XoZWxzNwZBoe5Wrx2S9nMfjbv5CckSN1OUSlrszCQ1xcHGQyGU6ePKlflpKSAplMhv379wPI+6Ysk8mwc+dO1K1bF2q1Gq1bt0ZiYiK2b9+OatWqwdHREX369EFm5r/fKAs7XTJ16lQMHDgQDg4OqFSpEpYsWWJQz5gxY1C1alXY2toiJCQE48ePR25ubqk81sfrOXv2LK5cuYIFCxagUaNGCAwMRNOmTTFlyhQ0atRIf5+bN2+iV69ecHZ2hqurK6KjoxEXF6e/XavVYuTIkXB2doabmxtGjx6N/v37F9ny88knn2Dy5Mlo0qQJKleujP/85z/o2LEjNm7caLCeTCaDt7e3/sfLy0t/mxACs2fPxrhx4xAdHY3atWvju+++w507d/StMOfPn8eOHTuwbNkyREVFoVmzZpg3bx7WrFmDO3fuPPfzWVLfH4lD57m/49St1HLbJ5Exdp9PxItzDiLmcpLUpRCVKpNoeZgwYQLmz5+PmJgY/Yfq7NmzsXr1amzduhW7du3CvHnzitzGjBkzUL9+fZw4cQJDhw7Fu+++i4sXL+pvd3BwwMqVK3Hu3DnMmTMHS5cuxaxZs0r9sXh4eEAul2P9+vXQarWFrpObm4sOHTrAwcEBhw4dwuHDh2Fvb4+OHTsiJydH/3hWrlyJ5cuX4/fff0dycjI2bdpkdD2pqalwdXU1WJaeno7AwEAEBAQgOjoaZ8+e1d927do1JCQkoG3btvplTk5OiIqKwpEjRwAAR44cgbOzM+rXr69fp23btpDL5Th69KjRNRor8WEWBqz4E+N/PotHuYU/x0Sm4m5aNl7/5iim77gAjZZ9ccg8mER4mDJlCpo2bYq6deti0KBBOHDgABYuXIi6deuiefPm6NmzJ/bt21fkNjp16oShQ4eiSpUqGDNmDNzd3Q3uM27cODRp0gRBQUHo0qULRo0ahZ9++qnY2l577TXY29sb/DzZj+Bxfn5+mDt3Lj799FO4uLigdevWmDx5Mq5evapfZ+3atdDpdFi2bBlq1aqFatWqYcWKFbhx44a+VWb27Nn4+OOP0b17d1SrVg2LFi2Ck5NTsfU+7qeffsJff/2FN998U78sLCwMy5cvx88//4wffvgBOp0OTZo0wa1btwAACQkJAGDQGpH/e/5tCQkJ8PQ0vGyxUqmEq6urfp2ysuNMAjrOPoT9HIJJFYhOAAv3X0GPRUdwg/1yyAyYRHioXbu2/v9eXl76UwuPL0tMTCzxNvKb5R+/z9q1a9G0aVN4e3vD3t4e48aNw40bN4qtbdasWTh58qTBT9euXYu8z3vvvYeEhASsWrUKjRs3xrp161CjRg389ttvAIBTp07h8uXLcHBw0AcSV1dXZGVl4cqVK0hNTUV8fDyioqL021QqlQbf9Iuzb98+vPnmm1i6dClq1KihX964cWP069cPERERaNmyJTZu3AgPDw8sXry4xNuWgkarw2c/n8E7PxzjOWSqsE7dTEGnuYew6cQtqUshei5lNqOJXJ6XSx6fRuJpfQysrKz0/5fJZAa/5y/T6Ypu7ivqPkeOHEHfvn0xceJEdOjQAU5OTlizZg1mzJhR7OPw9vZGlSpVDJY5ODgU2/nRwcEBXbp0QZcuXTBlyhR06NABU6ZMQbt27ZCeno7IyMhCWzA8PDyKrak4Bw4cQJcuXTBr1iz069evyHWtrKxQt25dXL58GUDe4wWAu3fvwsfHR7/e3bt3ERERoV/nyTCn0WiQnJysv39pSs7IwdBVx/DH1eRS3zZReUvP1uCDtadw6FISpnavBZWVQuqSiIxWZi0P+R+C8fHx+mWPd54sTzExMQgMDMTYsWNRv359hIaGFhhhUJZkMhnCw8ORkZEBAKhXrx5iY2Ph6emJKlWqGPw4OTnByckJPj4+Bv0HNBoNjh07Vuy+9u/fj86dO2P69OklGv2g1Wpx+vRpfVAIDg6Gt7c39uzZo18nLS0NR48eRePGjQHktV6kpKQY1LN3717odDqD1pLScO5OGrrO/53BgczOxhO30WvxEdxNyyp+ZSITU2bhQa1Wo1GjRpg2bRrOnz+PAwcOYNy4cWW1uyKFhobixo0bWLNmDa5cuYK5c+c+U+fDkjh58iSio6Oxfv16nDt3DpcvX8Y333yD5cuXIzo6GkDeMEd3d3dER0fj0KFDuHbtGvbv34/3339f3/fgP//5D6ZNm4bNmzfjwoULGDp0aLGtHfv27UPnzp3x/vvvo0ePHkhISEBCQgKSk//94J00aRJ27dqFq1ev4vjx43j99ddx/fp1DB48GEBe0BkxYgSmTJmCX375BadPn0a/fv3g6+urH+lRrVo1dOzYEUOGDMGff/6Jw4cPY9iwYejduzd8fX1L7bnc+k88eiyMwa0HvC4Fmad/bqUiev5hnLnNEUNUsZRpn4fly5dDo9EgMjJS/4Ekha5du+KDDz7AsGHDEBERgZiYGIwfP75M9uXv74+goCBMnDgRUVFRqFevHubMmYOJEydi7NixAABbW1scPHgQlSpV0neIHDRoELKysuDomHeBnQ8//BBvvPEG+vfvj8aNG8PBwQEvv/xykfv+9ttvkZmZiS+++AI+Pj76n+7du+vXefDgAYYMGYJq1aqhU6dOSEtLQ0xMDKpXr65fZ/To0Rg+fDjeeustNGjQAOnp6dixY4fBZFKrVq1CeHg42rRpg06dOqFZs2YFhsc+KyEEvtp5Ae+tPs7RFGT2EtKy8MqiI9hxJr74lYlMBK9tUYEMGDAAKSkphc56aS4eZuXig7Unsft80R1kicyNTAaMah+G916oUvzKRBIzidEWREDelTBfXhDD4EAWSQjgq50XMXLtSWRr2OJGpo3hgUzCqZsp6L4wBpcTpbk+BpGp2HjiNvouPYr76dlSl0L0VDxtQZL7PTYJb3//NzJy+G2LKJ+/ixrfD4pCsLud1KUQFcDwQJLadjoeI9acRA6n7SUqwMPBBqsHRyHUy3QuBEgEMDyQhH788wbGbjoNXgyT6Onc7Kzxw+AoVPNxlLoUIj2GB5LE8t+vYdKWc1KXQVQhONta4fuBUajlb9z1bYjKCsMDlbsF+y/jyx0Xi1+RiPQcVEp8N7Ah6lZykboUIoYHKl8zf7uEuXtipS6DqEKyt1FixZsN0CDIVepSyMIxPFC5mbHrIubtvSx1GUQVmq21Asv61UeTKu5Sl0IWjPM8ULn45vdrDA5EpSAzR4uB3/6FA5fuSV0KWTCGBypzm07cwpSt7BxJVFqycnV467u/8XccrzZL0mB4oDK170IiPlr3D3hyjKh0ZWt0GPzd35yVlSTB8EBl5tj1ZAxddRwaTuRAVCZSMnPRf/mfSEzLkroUsjAMD1QmLiY8xMCVf/OS2kRl7HbKI/Rf8RceZuVKXQpZEIYHKnU3kzPRb/lRpD7imxlReTgfn4Z3fziOXE7zTuWE4YFKVVJ6Nt745ijupvGKgETl6ffLSRi9/h9w9D2VB4YHKjVZuVoMXPkX4u5nSl0KkUXadOI2pu24IHUZZAEYHqjUjNt8Bv/cSpW6DCKLtvjAVaw8fE3qMsjMMTxQqfj+SBzWH7sldRlEBGDSlnM4fDlJ6jLIjDE80HM7dj2ZV8gkMiE6AfxnzQnc5RBOKiMMD/RcEtOy/tfLm520iExJUnoO3lt1HBqOwKAywPBAzyxXq8PQVceR+JAjK4hM0d/XH+CL7exASaWP4YGe2eQt5/D39QdSl0FERfjm92vYfjpe6jLIzDA80DNZf+wWvjtyXeoyiKgERq//B9eSMqQug8wIwwMZ7eydVIzddFrqMoiohB5ma/DuD8eQxeniqZQwPJBRsjVajFhzEtkadsIiqkguJDzE2E1npC6DzATDAxllxq5LiOUlgIkqpA3Hb+Gnv29KXQaZAYYHKrG/45Kx7NBVqcsgoucw6ddzuPWAU8jT82F4oBLJzNHgw3WnoON0DkQVWnq2hhfQoufG8EAl8sW2C7jOC14RmYWYK/fx/R8cLUXPjuGBivV7bBJ+OMo3GiJzMm37BVy/z+Gb9GwYHqhID7NyMXr9KbCFk8i8ZOZoMWYDT1/Qs2F4oCJN/PUc7qTy4jpE5uiPq8lY+xdHX5DxGB7oqfZdSORltonM3NRt55H4kF8QyDgMD1SoHI0OE349K3UZRFTG0rI0mPALX+tkHIYHKtQ3v1/j6AoiC7HtdAJ2n7srdRlUgTA8UAGJD7Pw9b7LUpdBROVo6rbzyNVy2nkqGYYHKuDLHReRnq2RugwiKkdXkzKwinM/UAkxPJCBf26lYMNxdpIkskRz9sQi9VGu1GVQBcDwQAYm/HKWczoQWagHmbk8ZUklwvBAeptP3MbxGylSl0FEEloZE4ebyewsTUVjeCAAeRe+mrb9gtRlEJHEcjQ6TN/B9wIqGsMDAQAW7r+ChDROFENEwJZ/4nH8xgOpyyATJhOc2NziPcjIQdPpe5GZo5W6lOdya+FAaNMSCyy3r9sZbu3f1f8uhEDiugnIunYMHi+PhW3Vxk/dZubFGDw8uR05CZehy3oInwFzYe0VYrBO8p6lyDizBzIrFZxb9od9jRf0t2Vc+B0ZZ/bAs+dnpfAIicpPvUrO2Di0qdRlkIlSSl0ASe+b369V+OAAAD79ZwG6f8ep5yRdR+LacbALN3wDfPj3z4CsZNvU5WbBxr86bMObIXnHvAK3Z14+iozzB+DZazI0D+7g/vY5UAfXg8LWCbrsDKQc/A5evac81+MiksLxGynY+k88Otf2kboUMkE8bWHh0rJy8e2ROKnLKBUKWyco7F30P48u/wmlsw9sAmrp18m5exVpf26C+4sjSrRN+5qt4dz0NaiDIgq9Pff+TagCasHGJxR21VtCZm0LTWreTH0P9q2AQ91OUDp6Pu9DI5LE7N2XeNVNKhTDg4VbeTgOD7PMb0Iooc1Fxrn9sK/dDjJZXjODLjcLSb9+Bdf270Jh71Iq+7H2CEZOwmVos9KRnXAZQpMNpYsvsm6dRc7dK3CI7FIq+yGSQmxiOvacL3gqkIinLSxYRrYGKw5fk7qMMpF56Q/ostJhV7ONftmDPctg41cNtqGNSm0/6pBI2NVohYRvP4BMaQ33zh9AbmWD5J0L4Nb5Azw8sQ0Pj2+BQu0I1w7DYO0RWGr7JioPCw9cQdvqXlKXQSaG4cGC/fDHdTzINM/Z5NL/2QV1SCSUDm4AgMzYo8i6cQo+A+aW+r6cm/WFc7O++t9Tfl8NVVAEZHIFUo+she/Ar/Ho8p+4v3UmfAbMKfX9E5WlY9cf4K+4ZDQIcpW6FDIhPG1hobJytVh6yDxbHTSpici6fgr2dTrol2VdPwXNgwTcnP0qrn/ZFde/7AoAuLf5CySs/m+p7Tv3/k1knNsH5+avI+vGaaj8a0Jh6wTb8ObIuXsFumxOvkMVz6L9V6QugUwMWx4s1I9/3kBSerbUZZSJ9NO/QWHrBHXlBvplTo1egX2d9gbrxS8fBpfWg6Gu0rBU9iuEwP2dX8Ol9WDIrdWA0EHo/tefJP9fwasWUsWz92IiLt19iKpeDlKXQiaCLQ8WKEejw5KDV6Uuo0wIoUP66d2wq9kGMrlCv1xh7wJrjyCDHwBQOnrAytlbv97tpe8g81KM/nfto4fIuXsVuUk3AAC5ybeQc/cqtOkFJ9BJP7UTCrUjbKtEAQBs/Koh6/o/yL59AWl//Qwrt0qQq+zL4mETlSkhgEUH2PpA/2LLgwXadOIW4lPNczbJrLiT0Kbdg33tds90f03yLYNTC48uH8X9bbP1vyf98iUAwKnpawb9HLQZD5B65Cd4v/6VfpmNbxgcG76MxPUTIbd1gnvnD56pJiJT8OupOxjVPgy+zmqpSyETwBkmLVCXeb/j9O1UqcsgogrmzaZB+KxLDanLIBPA0xYW5sztVAYHInoma/+6iVQzHaFFxmF4sDCr/7whdQlEVEFl5mix+eRtqcsgE8DwYEEyczT45eQdqcsgogps3bGbUpdAJoDhwYL8cvIO0rPNbypqIio/Z26n4dydNKnLIIkxPFgQnrIgotLA1gdieLAQZ26n4p9b7ChJRM/v55N3kKvlhGeWjOHBQvzIVgciKiXJGTnYfe6u1GWQhBgeLAA7ShJRaVt37JbUJZCEGB4swLbTCXjIjpJEVIoOXLqHxDTznKmWisfwYAG2n46XugQiMjNancDGE5zzwVIxPJi5jGwNDl1OkroMIjJD6/7mqAtLxfBg5vZfvIccDXtFE1Hpu3IvA5cTH0pdBkmA4cHM7TybIHUJRGTG9pxPlLoEkgDDgxnL0eiw7wJf2ERUdhgeLBPDgxmLuZLEURZEVKaO3XiAlMwcqcugcsbwYMZ2nuUkLkRUtrQ6gf0X70ldBpUzhgczpdMJ/MYZ4IioHOw+z/caS8PwYKaO33iApPRsqcsgIgtw8NI9aHitC4vC8GCmdrHVgYjKSVqWBn/GJUtdBpUjhgczdfASz0ESUfnZy1EXFoXhwQylZObg4l1O3EJE5WcPh4VbFIYHM/TH1WQIIXUVRGRJriVlID71kdRlUDlheDBDf1y9L3UJRGSBjl9PkboEKicMD2aI4YGIpHDixgOpS6BywvBgZlIf5bK/AxFJ4jjDg8VgeDAzJ248YH8HIpLEmTtpvIqvhWB4MDPHrzP5E5E0cjQ6nLmTKnUZVA4YHszM8RspUpdARBaMX2AsA8ODGdHpBE7eTJG6DCKyYCf4BcYiMDyYkSv30pHOS3ATkYTYadIyMDyYkdjEdKlLICILF5+ahYTULKnLoDLG8GBGYu8yPBCR9Hj61PwxPJiRy/cYHohIelf4XmT2GB7MSCwnhyIiExCXlCF1CVTGGB7MhE4ncI0vWCIyAXwvMn8MD2bi5oNMZHNmNyIyAXH3GR7MHcODmWBnSSIyFUnpOUjLypW6DCpDDA9mgp0liciUsN+DeWN4MBNseSAiU8J+D+aN4cFMsOWBiEwJw4N5Y3gwE3dSHkldAhGRHsODeWN4MANCCCRn5EhdBhGRHsODeWN4MAMPMnOh1QmpyyAi0ruTwutbmDOGBzOQlJ4tdQlERAZSH7E11JwxPJgBhgciMjW5WoH0bI3UZVAZYXgwA0npTPhEZHoesC+W2WJ4MAP32fJARCYo9RFnmTRXDA9mgKctiMgUPchky4O5YngwA/d52oKITNCDTLY8mCuGBzPAlgciMkUpbHkwWwwPZoATRBGRKUphy4PZYngwA9kandQlEBEVwD4P5ovhwQxwdkkiMkVseTBfDA9mQMPwQEQmKIetomaL4cEMsOWBiEwR35vMF8ODGdDomO6JyPSwVdR8MTyYAa2WL1AiMj06wfcmc8XwYAaY7onIFPG0hflSSl0APT+meyottRzT0b7GIfymTYAAjyt6Pr6utQE0lLoMKgMMD2aALQ9UWoJUjzD8xPcICX8Bn+vu4mFuutQlUQUW4OgldQlURnjawgywzwOVFg+rLABA5wv7sC4pHXWdqkhcEVVkCplC6hKojDA8mAOZ1AWQuXBTZun/75d8AytOHcBQp1r8EKBnopDzuDFXDA9mwMGGZ5+odLgqsgx+Vwgt3j25FStzneBnyyZoMo5cxo8Yc8W/rBmwVzE8UOlwlj8qdHnEzZNYf+UiXnKpWc4VUUXGFivzxfBgBhxUVlKXQGbCUVZ4eAAA+6w0fHF8G6bZVIa9lV05VkUVlR2PE7PF8GAGHNjyQKXEARnFrtP5wj6sT8pgZ0oqlrONs9QlUBlheDAD9uzzQKXEHpklWo+dKakkGB7MF8ODGeBpCyotal3xLQ/52JmSiuOscpa6BCojDA9mwJGnLaiUqLTGTwrFzpT0NC42LlKXQGWE4cEM8LQFlRYbzbPNKMnOlFQYtjyYL4YHM8AOk1RalJqHz3X//M6UEY6VS6kiqsjY8mC+GB7MAPs8UGlR5jxfeADyOlOu/OcgO1MSWx7MGMODGfBwsJG6BDITsuy0UtnOv50pndmZ0kIpZUo4WjtKXQaVEYYHMxDgait1CWQG7JRayLTZpbrNiJsnsP7KRXRmZ0qL42jD4GDOGB7MgJ+zGnJeHIuek69NTpls1z4rDdOOb8MXNlXYmdKCsL+DeWN4MAPWSjm8HVVSl0EVnLdN6bY6POmlC3vZmdKCuKvdpS6ByhDDg5nw56kLek6eVmUbHgB2prQkQU5BUpdAZYjhwUxUYnig5+ReDuEBYGdKSxHsFCx1CVSGGB7MRIALwwM9HzdlVrnuj50pzVuwI8ODOWN4MBOV3NRSl0AVnIu8ZBfFKk3sTGm+2PJg3jg1oZlgywM9L2d5+bY8PO6lC3sR4VoJHwdUxsm0K5LV8TTaR1okbkxE2vE0aNI0UAWq4NPHB7YhT3/d3d99H8l7kpGTlAMrNyt4dPGAS9N/RyCkn0nHne/vQJOqgUNdB/gN8oNcmfd9TpupxZWJVxD0URCs3a3L/PGVNrVSDW87b6nLoDLElgczwbke6Hk5yh5Jun///3WmfNexpsl1pry94jbSz6bD/y1/VJlSBfY17BH3VRxyH+QWuv79vfdxd/1deHbzROjnofDs5on47+ORdiJvEi6hE7i5+CZcX3BFyPgQZMVl4cH+B/r73113F64vuFbI4AAAQY5BkMk4ftycMTyYCU8HG6itTOsNlyoWezzbRbFKk0JoMfTUNpPqTKnL0SHt7zR49/KGXZgdbLxs4PWyF6w9rZG8N7nQ+6TEpMC1lSucopxg7WkN50bOcGnpgqRtSQAAbboW2odauLZ2hcpPBYe6Dsi+k9dhNTM2E5nXMuHW3q3cHmNp40gL88fwYCZkMhmq+ThIXQZVYHai/Ps8PI0pdaYUWgHoAJm14TdpubUcGZcyCr9ProDMquD6j64+gtAIKBwUUDorkX42HbpsHTIuZUAVoILQCNz57g78BvhBVoFnfmN/B/PH8GBGavs7S10CVWBqXeEfhFIxlc6UCrUC6ipqJP6ciNwHuRA6gZSYFGRezoQmVVPofexr2ePBwQd4FPcIQgg8uvYIDw48gNAKaNI1kMlkCBgagHu/3EPs2FioK6nh0twF97beg124HWRWMlydchWX/nsJ93ffL+dH/PwYHswfO0yakdr+TlKXQBWYSiv9aYvCmEJnSv+3/HH7m9u4+MFFQA6oA9VwauSErLjCO5l6dvWEJlWDK5OvAAJQOirh3MwZSduS9H0B7KraofJn/862mZ2QjZSYFFSeWBnXvrgGt3ZucKjtgNixsbALs4MqoOLMIsthmuaP4cGMMDzQ87DWmGZ4AP7XmfLBbSyu0xFL0s5BK7Tlun8bTxuEfBwCXbYO2kdaWDlb4caCG7DysCp0fbm1HP6D/OHX3w+aNA2Uzkok70+GXCWHwqHwvkl3Vt6Bd29vQABZ17Pg1MAJchs57MLskHEho8KEB4VMwT4PFoCnLcxIiLs97G2YB+nZKHMfSl1CkRRCi6Ent2KFhJ0p5TZyWDlbQZuhRfrpdDjWK/rKkTKlDFauVpDJZUg9mgqHCIdC+zIkH0iGwk4Bx7qOgC5vmdAK/b9CJ0r9sZSVqi5VYaOwkboMKmMMD2ZELpehhi8vg0vPRmHi4SFf3ZsnsO7KJXQqx86UD08/xMN/HiLnXg7Sz6Tj2rRrsPGxgUuzvHkbEtYl4NaSW/r1809BZCdkI/NqJm4uuInsW9nw6lEw9GjSNLj36z34vO4DAFDYKWDja4P7u+4j83ImMs5nwDa04gzFjvSKlLoEKgf8mmpm6gQ44+i1woePERVFlp0mdQkl5pCViunHt6F5eGt8rotHem7ZdvbUPdIhYV0CNA80eS0E9R3h1cMLMmVeK4ImRYOc+/9e0lzoBJJ2JCE7IRsyhQx24XYIGRcCa4+C8zbEr4qHe0d3WLn8ewrEb7Afbi+9jfu/3Yf7i+5FTkZlaup51ZO6BCoHMiFExWkPo2L9euoOhv94QuoyqIJxs87FMXl/qct4JrdcK+G/AZVxygRnprRE+3vth5u64s5RQSXD0xZmpg6Ha9Iz8LbJKX4lE+WffAPf/nMQ7/Iy35ILdAxkcLAQDA9mppKbLZxtC+8BTvQ0XtYVNzwAptGZkoB6njxlYSkYHsxQ/UBXqUugCsbTWrqLYpUmKTpT0r/Y38FyMDyYoZZV3aUugSoYd2W21CWUmvzOlFNtKvMy3+WMLQ+Wg+HBDLUK85S6BKpgXJXSXlGzLHS5sA/rkjJQx7Fy8SvTc3NXu6OSYyWpy6BywvBghgJcbRHszm9cVHIucvMLDwA7U5YntjpYFoYHM9WyqofUJVAF4mSm4QEw7Ezpq2arXFlhfwfLwvBgplqGMTxQyTnKzDc85Kt78wTWX41lZ8oy0syvmdQlUDlieDBTjUPcYKPkn5dKxl6Y1uW4y4q+M6VK2st8m5tgp2AEOgZKXQaVI366mCmVlQINgzlkk0rGzkLCQ74u5/eyM2UpahXQSuoSqJwxPJgx9nugklLpLCs8AP+7zPfpQ3iHnSmfW+uA1lKXQOWM4cGMtWK/ByohG0261CVIQqnT4D12pnwuripX1PaoXWrba9WqFUaMGKH/PSgoCLNnzy617Zd0v6YgLi4OMpkMJ0+elLqUAhgezFgVTwcEuKqlLoMqAGtNxbgcd1nJ70z5IjtTGq1VQCvIZc/+UTJgwAB069at9Ap6Rhs3bsTkyZPLfD8ymQwymQx//PGHwfLs7Gy4ublBJpNh//79AICAgADEx8ejZs1/j8tNmzahUaNGcHJygoODA2rUqCFJ6GF4MHMv1faVugSqAJS5lh0egLzOlF+yM6XR2ge2l7qEUuHq6goHB4dy2VdAQABWrFhhsGzTpk2wt7c3WKZQKODt7Q2lUgkA2LNnD1599VX06NEDf/75J44dO4bPP/8cubm55VL34xgezFzXOgwPVDxFDsNDPnamLDlnG2dE+USV2/4Ka8ZPSUkx+La+f/9+yGQy7Ny5E3Xr1oVarUbr1q2RmJiI7du3o1q1anB0dESfPn2QmZmp305hp0umTp2KgQMHwsHBAZUqVcKSJUsM6hkzZgyqVq0KW1tbhISEYPz48SX6IO/fvz/WrFmDR4/+HSK9fPly9O/fv8jH++uvv6Jp06b46KOPEBYWhqpVq6Jbt274+uuvDe73888/o169elCpVAgJCcHEiROh0Wj0t8fGxqJFixZQqVSoXr06fvvtN8hkMmzevLnY2vMxPJi5aj6OqOplX/yKZLHkMh2QzfDwOHamLJk2ldpAKVdKXUahJkyYgPnz5yMmJgY3b95Er169MHv2bKxevRpbt27Frl27MG/evCK3MWPGDNSvXx8nTpzA0KFD8e677+LixYv62x0cHLBy5UqcO3cOc+bMwdKlSzFr1qxia4uMjERQUBA2bNgAALhx4wYOHjyIN954o8j7eXt74+zZszhz5sxT1zl06BD69euH//znPzh37hwWL16MlStX4vPPPwcA6HQ6dO/eHdbW1jh69CgWLVqEMWPGFFvzkxgeLABbH6goXjYayCCkLsPk5HemXK5xYWfKp2gfZLqnLKZMmYKmTZuibt26GDRoEA4cOICFCxeibt26aN68OXr27Il9+/YVuY1OnTph6NChqFKlCsaMGQN3d3eD+4wbNw5NmjRBUFAQunTpglGjRuGnn34qUX0DBw7E8uXLAQArV65Ep06d4OFRdCf34cOHo0GDBqhVqxaCgoLQu3dvLF++HNnZ/17YbuLEifjvf/+L/v37IyQkBO3atcPkyZOxePFiAMDu3btx4cIFfPfdd6hTpw5atGiBqVOnlqjmxzE8WICudfykLoFMmI9NjtQlmLR6N46zM2UhXGxcEOVdfqcsjFW79r8jQLy8vPSnFh5flpiYWOJtyGQyeHt7G9xn7dq1aNq0Kby9vWFvb49x48bhxo0bJarv9ddfx5EjR3D16lWsXLkSAwcOLPY+dnZ22Lp1Ky5fvoxx48bB3t4eH374IRo2bKg/BXPq1ClMmjQJ9vb2+p8hQ4YgPj4emZmZOH/+PAICAuDr+++XysaNG5eo5scxPFiASm62aBjECaOocJ7W5nM57rLyeGdKO6Wt1OWYhK6Vu0IhL99TOnJ53keWEP+2lD2tj4GVlZX+/zKZzOD3/GU6na7I/RV1nyNHjqBv377o1KkTtmzZghMnTmDs2LHIySlZGHdzc8NLL72EQYMGISsrCy+++GKJ7gcAlStXxuDBg7Fs2TIcP34c586dw9q1awEA6enpmDhxIk6ePKn/OX36NGJjY6FSqUq8j+KY5skqKnU9I/3xZ1yy1GWQCfKwypK6hAqjy/m9iHALxMf+lXEq7YrU5UhGBhl6hfUq9/3mN+vHx8ejbt26ACDZHAgxMTEIDAzE2LFj9cuuX79u1DYGDhyITp06YcyYMVAoni2IBQUFwdbWFhkZeRO91atXDxcvXkSVKlUKXb9atWq4efMm4uPj4ePjAwAFho2WBMODhehc2wcTfj2LzByt1KWQiXFXsuXBGAH3r2Plg9tYXLsDlqadg1ZY3msqyicKlRwrlft+1Wo1GjVqhGnTpiE4OBiJiYkYN25cudcBAKGhobhx4wbWrFmDBg0aYOvWrdi0aZNR2+jYsSPu3bsHR0fHEq0/YcIEZGZmolOnTggMDERKSgrmzp2L3NxctGvXDgDw6aef4qWXXkKlSpXQs2dPyOVynDp1CmfOnMGUKVPQtm1bVK1aFf3798dXX32FtLQ0gwBUUjxtYSHsbJToWNNb6jLIBLkqzP+KmqXN0jtTvhr2qmT7Xr58OTQaDSIjIzFixAhMmTJFkjq6du2KDz74AMOGDUNERARiYmIwfvx4o7Yhk8ng7u4Oa2vrEq3fsmVLXL16Ff369UN4eDhefPFFJCQkYNeuXQgLCwMAdOjQAVu2bMGuXbvQoEEDNGrUCLNmzUJgYN6Fy+RyOTZt2oRHjx6hYcOGGDx4sH4khlG1i8dPHpFZ++PqffReYnzzFJm3eZX/RpfbM6Uuo8J6qHLC5OpNsf3B04fPmRNPtSd29txpskM06dnIZDJs2rSpxLN9suXBgjQKcUM1n5I1j5HlcJKz5eF5WFpnyu5VuzM4EMODpRnULFjqEsjEOMgyi1+JitXl/F6sS36E2mY8M6VCpkCP0B5Sl0EmgOHBwnSt4wtPBxupyyATYi8YHkpLwP3r+Pb0IbxtpjNTtvBvAW879p0yR0IIoy5QxvBgYayVcvRvEiR1GWRCbHWWeTnusqLUaTDMTDtTSjE8k0wTw4MFej0qELbW5vetiJ6NSpchdQlmydxmpvS390dT36ZSl0EmguHBAjnZWqFnpL/UZZCJsNGw5aGsmFNnyt7hvSGTyaQug0wEw4OFGtg0GHK+DxAAKw2vqFnWKnpnSjeVG09ZkAGGBwsV5G6HttW8pC6DTIAyh+GhPFTkzpRv1nwTaqVa6jLIhDA8WLAhLUKKX4nMnpzhodxUxM6UbHWgwjA8WLAGQa6oV8lZ6jJIQjZyHWS57DBZ3ipSZ0q2OlBhGB4s3OiO4VKXQBLytinZ5YOp9FWEzpTuandJr2NBpovhwcI1CnFDqzAPqcsgiTA8SM+UO1MOrDkQKqVK6jLIBDE8EMZ0DOfICwvlac3LcZuC/M6UbznVglxmGm/L7mp3vFL1FanLIBNlGkcpSaqajyOiI/ykLoMk4GGVJXUJ9D9KnQbDT27Fco2bSXSmHFRzEFsd6KkYHggA8GH7qrBW8nCwNO5KhgdTE3njmOSdKT3UHngljK0O9HT8tCAAgL+LLV6PCpS6DCpnLgqGB1OU35nyc1WoJJ0pB9caDBsFL6BHT8fwQHrDW1eBg41S6jKoHDnLH0ldAhWh6/k9WJechdqO5TcnS6hLKOd1oGIxPJCei5013m7JiaMsiZOMl+M2dQH34/Dt6d/LrTPl2KixUMr5JYKKxvBABgY1C4GnA5srLYUDw0OF8HhnSh912Q2t7hLSBZFekWW2fTIfDA9kQG2twNjO1aQug8qJnSi78HDwugZdfsyE74yHkE1Mw+YLuQXWOX9Pi64/ZsJpWhrspqahwdJ03EjVPXWbZxO16PFTJoJm521z9h8Fh5qu+icXAbMewmV6GkbuNOzTEZeiQ9V56UjLFs//ACUQeeMY1l+7jI4uNUp92w5WDhhZf2Spb5fME8MDFRAd4YeWVTlxlCVQ68ructwZOQJ1vOT4ulPhw/2uJOvQbEUmwt3l2N/fDv+8Y4/xLWygKqLFPDMXCHGWY1pbFbztC05OkpSpw+BfH+H/2qmw63U7/PBPLrZc+je0DN2ahWltbeBoU3EnNnF8lIqvjm8v9c6U79V9D+5q91LbHpk3ntiiQn3+ck20n3UQmTlaqUuhMqTSlt11LV4MtcKLoVb/+61gx8yxe7PQKVSJL9v9Gy4quxb9faaBnwIN/PKuSPnf3QVHilx9IOBkI8OrNfP2+0KwAufv6fBSVeDH07mwUgDdq1kVuF9F1PX8HtR1C8J//UPwT9rV59pWuGs4eof1LqXKyBKw5YEK5e9ii5HtqkpdBpUxG03ZtTwURScEtsZqUNVVjg4/ZMDzq4eIWpZe6KkNY4S6ypGZK3AiXovkRwJ/3daitpcCDx4JjN+XhfkvmtekR6XRmVIGGcZGjYVCXrEuE07SYnigp3qzaTBq+ztJXQaVIaVGmstxJ2YIpOcA0w5no2NlJXa9YYuXw63Qfe0jHIjTPPN2XdQyfNtNjX6bH6Hh0nT0q2OFDlWUGLUrC8MaWuNaig51F6ej5oJ0rD/3fEHFVDxvZ8roKtGI8Iwo/cLIrPG0BT2VQi7DF91rIXr+YWh0FbODGRVNmZMmyX7zD6foMCU+aJw3uifCW4GYm1osOpaDlkHP/tb0cjUrvPzYqYkDcRr8k6jFvE4qVJmbjh97qOFtL0PDZRloEaiAp515fIeKvHEM69VOmFytCXY8OFui+zhaO+KDyA/KuDIyR+bxqqEyU8PXCYOaBUtdBpURWbY0LQ/utjIo5UB1D8Om8mructxILb2gmq0RGLotC4tfUuNysg4aHdAySIkwdwWquslx9JZ59enJ70w5RRUK2xJ0phwROQKuKtdyqIzMDcMDFeuDdlVRybX8p8ilsmWn1EKmleaqmtYKGRr4KnDxvuGwzEvJOgQ6ld5IiCkH806L1PNRQKuDQQtarhbQmmmDWvT5PVhfzMyUzf2a86qZ9MwYHqhYKisFpr5cS+oyqJT52ZRtcEjPETiZoMXJhLxv99ce6HAyQaufx+GjJtZYeyYXS4/l4HKyDvP/zMGvFzUY2sBav41+mx7h48dGVeRo/91mjha4nZb3++XkgnNDnLunxdqzGkx6Ie+0SLi7HHKZDN8cz8HWS7m4kKRDA1/z7SRYVGdKFxsXTGo6SaLKyBzIhBBmmr2ptH328xl8e+S61GVQKWnhmoLvMoeW2fb3x2nwwrcFJ6HqX8cKK7upAQDLT+Tgi99zcCtNhzA3OSa2skF0+L/9FVqtzECQs1y/flyKDsFzCo4QaRmowP4BdvrfhRBoviIT/21mjZeq/ru9LZdy8d62LGRrgCmtbTC4nnWBbZmjY5Ui8bE9EP/oHgBgdqvZaBPYRuKqqCJjeKASy9Zo0X1BDM7ekaaTHZWuHl53MSOVneUsRdr/OlNau1bG580+l7ocquB42oJKzEapwPw+9WDPK2+aBXcrafo7kDQcH6Xiq2sXMKHBx1KXQmaA4YGMEuxuh89fril1GVQK3JS8HLdFkVsBPb+BlY291JWQGWB4IKNFR/ihd4MAqcug5+QqLzi9M5mxNp8CfrxiJpUOhgd6JhO61kCYl4PUZdBzcJKz5cFiVG4DNBkudRVkRhge6JmorBT4um9dqK3Md6ibuXOUld3luMmEOAcCPZYBsop7JVEyPQwP9MyqeDpgYnQNqcugZ+SAsruiJpkIKzvgtR8BW84iSaWL4YGeS6/6AehRz1/qMugZ2Aq2PJg3GfDyIsCLAZ9KH8MDPbep3WuiYRC/2VQ0ah1bHsxay9FA9a5SV0FmiuGBnpuNUoEl/SIR4m5X/MpkMlRaaS6KReUg/CWgFedzoLLD8EClwtnWGssHNICrnWVM92sOrDVseTBLntWBlxezgySVKYYHKjVB7nZY8kYkrJU8rCoCZS5bHsyO2hXovRrgRFBUxvguT6WqfpAr/u+VOvzSUwEocniNErOiVAGv/gC4BktdCVkAhgcqdV3r+GJU+zCpy6BiyHLY8mA25ErglW+BoKZSV0IWguGBysR7L1RBr/ocwmmq3KxzIdNppC6DSoUMiF4AhHWUuhCyIAwPVGY+f7kWmoe6S10GFcLbJkfqEqi0dJwG1HlV6irIwjA8UJmxUsix5I36aBTCOSBMjZc1L8dtFlqOARq9I3UVZIEYHqhMqa0VWDGgIaKCGSBMiSfDQ8XX8C3ghU+kroIsFMMDlTm1tQIr32SAMCXuSoaHCq3WK8CLX0pdBVkwhgcqF2prBVa82QANGSBMgpuCl+OusGr2BLot4iRQJCmGByo3ttZKrGSAMAkuSoaHCqleP6D7UkChlLoSsnAMD1Su9AGCF9KSlKOM4aHCiXoX6DIXkPNtm6THo5DKna21EisHMkBIieGhgmk+CnhxGk9VkMlgeCBJ2ForseLNBpwHQiL2ghfFqjDaTgDajJe6CiIDDA8kGTsbJVYMaIBX6wdIXYrFsWN4qABkQKf/A5p9IHUhRAUwPJCklAo5pvesjVHtq7JFthypdAwPJk1hDXRbCDQcInUlRIVieCCTMKx1KGa/GgFrBQ/J8qDS8KJYJkvtCryxGYh4TepKiJ6K79RkMqIj/PD9oIZwtrWSuhSzZ6VJl7oEKox7GDBkL6+OSSaP4YFMSlSIGza82wSVXG2lLsWsKXPZ8mByKrcBBv8GuAZLXQlRsRgeyORU9rDHpqFNEBHgLHUpZkuRw/BgUhq+DfRdB6icpK6EqEQYHsgkudnbYM1bjRAd4St1KWZHLtMB2QwPJkGuBDrPADp9CcgVUldDVGIMD2SyVFYKzOldF5Oja7AjZSnystFABiF1GWTnAby+AWgwWOpKiIzGd2QyeW80DsK6dxrDz1ktdSlmwccmR+oSKLgl8M5hIKSV1JUQPROGB6oQ6gQ4Y+v7zdAm3FPqUio8L+ssqUuwXDIF0Hpc3lBMBy+pqyF6ZgwPVGE421rjmwEN8OlL1Xka4zl4WLHlQRKO/sCb24AWH/HiVlTh8QimCmdgs2Bseq8JQjzspC6lQnJTsuWh3IV1Bt45BFRqJHUlRKWC4YEqpBq+TtgyvBmvi/EMXBW8oma5UdgAL34JvLYasOVVZMl8MDxQhWVrrcT0nrWx8s0G7ExpBGc5w0O58G8IvH0QiHpb6kqISh3DA1V4rcI88dvIFhjYNBhyXlyrWE4MD2XL2j6vtWHgTsAzXOpqiMoEwwOZBVtrJT7tUh2bhjZFuLeD1OWYNAdZptQlmK8q7YChf+S1NrBTJJkxHt1kVuoEOGPL8Gb4qEMYbJQ8vAtjL3g57lJn6wZ0Xwq8vh5wZj8cMn98dyWzo1TI8d4LVbBjRAs0CmEntSfZ6hgeSlWtV4D3/gJq95K6EqJyw/BAZivY3Q4/DmmE6T1qwc3OWupyTIaK4aF0+EQA/bcAPZYBdm5SV0NUrmRCCE5yT2YvPVuDxQeu4JvfryEzRyt1OZI6E/B/sL93XOoyKi5HP6DNp0DtVwEZe+iSZWJ4IIuS+DALc3bHYu1fN6HRWeahf9HnM9g8iJW6jIrH2gFoNgJo/B5gxaHBZNkYHsgiXb2Xjq92XsT2MwlSl1LurriNhCLD8h73M5MpgHr9gBc+Aex5bRUigOGBLNyJGw/wxfYL+PNastSllJtrDkMgy2W/h+LJgLBOeacoOF8DkQGGByIAey/cxVc7L+F8fJrUpZQpG7kOF61fl7oMEycDqnfNu4CVdy2piyEySQwPRI85FHsPSw9dw8FL96QupUwEqbOwXwyUugzTJFMANV4GWowCPKtJXQ2RSVNKXQCRKWke6oHmoR64mPAQyw5dxc8n7yBHq5O6rFLjZZMD8KKahpRqoO7rQJNhgEuQ1NUQVQhseSAqQmJaFr49EodVR28gJTNX6nKeWxfPe5iX9h+pyzAN9l5A5ACg4VuAnbvU1RBVKAwPRCXwKEeLdcdu4pvfr+H6/Yp7bYiBfjfx6f0xUpchIRkQ0gqo/2ZeZ0iFldQFEVVIPG1BVAJqawX6NQ7C61GBOBB7DxuP38Zv5xKQlVuxTmm4Ky30nIWdJ1C3L1CvP+AaLHU1RBUewwOREeRyGV4I88QLYZ54mJWLbafjsfH4bfwZl4yK0IbnorCk8CADglvktTKEv8RWBqJSxPBA9IwcVFZ4tUElvNqgEm49yMSm47ex6cRtXE0y3TkUnOUV95RLiflFAtWjgerdAJdAqashMkvs80BUyk7ceIBNJ25j2+l4JKXnSF2OgdWh+9Hk5hKpyyhlMsC/wf8CQzQviU1UDhgeiMqIEAKnb6di/8V72HcxEadupkDqy2n8WnUbat34QdoiSoUMCIgCanQDqnUFnPwkqaJVq1aIiIjA7NmzAQBBQUEYMWIERowYUa77lZqp1UNlj5fkJiojMpkMtf2d8X6bUGwa2hTHxrXDnN4R6F7XD+720lwi3E5U4NMWLsF5Qyt7rgA+ugwM2gk0erdcg8OAAQPQrVu3ctvf02zcuBGTJ08u8/3IZDJs3ry5wPInn4cn67l27Rr69OkDX19fqFQq+Pv7Izo6GhcuXCjzmvNdvnwZDg4OcHZ2LnDbunXrEB4eDpVKhVq1amHbtm0Gtwsh8Omnn8LHxwdqtRpt27ZFbKzhxeSSk5PRt29fODo6wtnZGYMGDUJ6enpZPiSTwj4PROXExc4a0RF+iI7wM2iVOHjpHk7fTkW2puxHbqh1FejNzd4rr8NjcEsgpCXgXEnqikyGq6ur1CUYeLye3NxctGvXDmFhYdi4cSN8fHxw69YtbN++HSkpKeVST25uLl577TU0b94cMTExBrfFxMTgtddewxdffIGXXnoJq1evRrdu3XD8+HHUrFkTAPDll19i7ty5+PbbbxEcHIzx48ejQ4cOOHfuHFQqFQCgb9++iI+Px2+//Ybc3Fy8+eabeOutt7B69epyeYxSY8sDkQQeb5VY/24TnJ3YAVuGN8PUl2uhd4MAhHs7QCGXlfp+VVoT7cypsAa8awN13wA6zwCG/gGMugT0WAbUe6NCBoe4uDjIZDKcPHlSvywlJQUymQz79+8HAOzfvx8ymQw7d+5E3bp1oVar0bp1ayQmJmL79u2oVq0aHB0d0adPH2Rm/ttq1KpVK4NTI0FBQZg6dSoGDhwIBwcHVKpUCUuWGPZtGTNmDKpWrQpbW1uEhIRg/PjxyM0tnYnPHq/n7NmzuHLlChYsWIBGjRohMDAQTZs2xZQpU9CoUSP9fW7evIlevXrB2dkZrq6uiI6ORlxcnP52rVaLkSNHwtnZGW5ubhg9ejT69+9fopafcePGITw8HL169Spw25w5c9CxY0d89NFHqFatGiZPnox69eph/vz5APJaHWbPno1x48YhOjoatWvXxnfffYc7d+7oW2HOnz+PHTt2YNmyZYiKikKzZs0wb948rFmzBnfu3Hnm57EiYXggMgFKhRw1/ZzQJ6oSpvWojR0jWuDMhA5Y/05jjOtcDV3r+CLIzRay58wTNhoTaHlQqgG/+kD9QUCXucBbB4CPbwPvHAKi5wMNBlvctSUmTJiA+fPnIyYmRv+hOnv2bKxevRpbt27Frl27MG/evCK3MWPGDNSvXx8nTpzA0KFD8e677+LixYv62x0cHLBy5UqcO3cOc+bMwdKlSzFr1qxSfyweHh6Qy+VYv349tFptoevk5uaiQ4cOcHBwwKFDh3D48GHY29ujY8eOyMnJ0T+elStXYvny5fj999+RnJyMTZs2Fbv/vXv3Yt26dfj6668Lvf3IkSNo27atwbIOHTrgyJEjAPJOuSQkJBis4+TkhKioKP06R44cgbOzM+rXr69fp23btpDL5Th69GixNZoDnrYgMlFqawXqB7miftC/TcJZuVrcTM7Ejcd+biZn4vr9TNx8kFnspFXK3HK6aqiNI+AcmDdU0iXof/8PyvtxqwzIFeVTRwUxZcoUNG3aFAAwaNAgfPzxx7hy5QpCQkIAAD179sS+ffswZszTZwft1KkThg4dCiCvlWHWrFnYt28fwsLCAOR9G88XFBSEUaNGYc2aNRg9enSRtb322mtQKAz/XtnZ2ejcuXOh6/v5+WHu3LkYPXo0Jk6ciPr16+OFF15A37599Y9n7dq10Ol0WLZsGWT/S8QrVqyAs7Mz9u/fj/bt22P27Nn4+OOP0b17dwDAokWLsHPnziJrvX//PgYMGIAffvgBjo6Oha6TkJAALy8vg2VeXl5ISEjQ356/rKh1PD09DW5XKpVwdXXVr2PuGB6IKhCVlQKhXg4I9XIo9PbEtKy8QPEgE/fTc/AgMwcpmblIeZSLlMwcyJSBgFIJ5KQDORl5/4r8wCFDXtNG/r+PLVOqAJUzoHb637/OgNrl3/+rnAFbt7zTCy5BgK1pnZM3dbVr19b/38vLS39q4fFlf/75Z4m3IZPJ4O3tjcTERP2ytWvXYu7cubhy5QrS09Oh0Wie+gH7uFmzZhX4pj5mzJintioAwHvvvYd+/fph//79+OOPP7Bu3TpMnToVv/zyC9q1a4dTp07pOzQ+LisrC1euXEFqairi4+MRFRWlv02pVKJ+/fooaoDgkCFD0KdPH7Ro0aLYx0XPh+GByIx4Oqrg6agyaK0w9HO51mPJ5PK8s8KPf9g9rY+BldW/s1/KZDKD3/OX6XRFtyoVdZ8jR46gb9++mDhxIjp06AAnJyesWbMGM2bMKPZxeHt7o0qVKgbLHBwciu386ODggC5duqBLly6YMmUKOnTogClTpqBdu3ZIT09HZGQkVq1aVeB+Hh4exdb0NHv37sUvv/yC//u//wOQ99zrdDoolUosWbIEAwcOhLe3N+7evWtwv7t378Lb21v/ePOX+fj4GKwTERGhX+fxYAYAGo0GycnJ+vubO/Z5ICIqA/kfgvHx8fplj3eeLE8xMTEIDAzE2LFjUb9+fYSGhuL69evltn+ZTIbw8HBkZOR12K1Xrx5iY2Ph6emJKlWqGPw4OTnByckJPj4+Bv0HNBoNjh07VuR+jhw5gpMnT+p/Jk2aBAcHB5w8eRIvv/wyAKBx48bYs2ePwf1+++03NG7cGAAQHBwMb29vg3XS0tJw9OhR/TqNGzdGSkqKQT179+6FTqczaC0xZ2x5ICIqA2q1Go0aNcK0adMQHByMxMREg34H5Sk0NBQ3btzAmjVr0KBBA2zdurVEnQ+fxcmTJ/HZZ5/hjTfeQPXq1WFtbY0DBw5g+fLl+j4bffv2xVdffYXo6GhMmjQJ/v7+uH79OjZu3IjRo0fD398f//nPfzBt2jSEhoYiPDwcM2fOLLa1o1o1w462f//9N+RyuX4IJgD85z//QcuWLTFjxgx07twZa9aswd9//60fnSKTyTBixAhMmTIFoaGh+qGavr6++pEe1apVQ8eOHTFkyBAsWrQIubm5GDZsGHr37g1fX9/SezJNGFseiIjKyPLly6HRaBAZGan/QJJC165d8cEHH2DYsGGIiIhATEwMxo8fXyb78vf3R1BQECZOnIioqCjUq1cPc+bMwcSJEzF27FgAgK2tLQ4ePIhKlSqhe/fuqFatGgYNGoSsrCx9P4wPP/wQb7zxBvr374/GjRvDwcFB33rwPJo0aYLVq1djyZIlqFOnDtavX4/NmzcbBIzRo0dj+PDheOutt9CgQQOkp6djx44d+jkeAGDVqlUIDw9HmzZt0KlTJzRr1qzA8FhzxumpiYioQhgwYABSUlIKnfWSyhdbHoiIiMgoDA9ERERkFJ62ICIiIqOw5YGIiIiMwvBARERERmF4ICIiIqMwPBAREZFRGB6IiIjIKAwPREREZBSGByIiIjIKwwMREREZheGBiIiIjMLwQEREREZheCAiIiKjMDwQERGRURgeiIiIyCgMD0RERGQUhgciIiIyCsMDERERGYXhgYiIiIzC8EBERERGYXggIiIiozA8EBERkVEYHoiIiMgoDA9ERERkFIYHIiIiMgrDAxERERmF4YGIiIiMwvBARERERmF4ICIiIqMwPBAREZFRGB6IiIjIKAwPREREZBSGByIiIjIKwwMREREZheGBiIiIjMLwQEREREZheCAiIiKjMDwQERGRURgeiIiIyCgMD0RERGQUhgciIiIyyv8Dk5OtjWyjuKsAAAAASUVORK5CYII=", "text/plain": [ "

" ] @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 7, "id": "0fee9320-4354-47ce-afbc-e44b2ef09c50", "metadata": { "tags": [] @@ -616,20 +616,20 @@ " \n", " type\n", " id\n", - " attributes.experiment-type\n", " attributes.accession\n", " attributes.analysis-status\n", + " attributes.experiment-type\n", " attributes.pipeline-version\n", " attributes.is-private\n", " attributes.complete-time\n", " attributes.instrument-platform\n", " attributes.instrument-model\n", - " relationships.study.data.id\n", - " relationships.study.data.type\n", - " relationships.run.data.id\n", - " relationships.run.data.type\n", " relationships.sample.data.id\n", " relationships.sample.data.type\n", + " relationships.run.data.id\n", + " relationships.run.data.type\n", + " relationships.study.data.id\n", + " relationships.study.data.type\n", " Submitted nucleotide sequences\n", " Nucleotide sequences after format-specific filtering\n", " Nucleotide sequences after length filtering\n", @@ -648,26 +648,26 @@ " \n", " 0\n", " analysis-jobs\n", - " MGYA00646965\n", - " amplicon\n", - " MGYA00646965\n", + " MGYA00585242\n", + " MGYA00585242\n", " completed\n", + " amplicon\n", " 5.0\n", " False\n", - " 2023-07-19T14:11:39\n", - " LS454\n", - " 454 GS FLX\n", - " MGYS00006253\n", - " studies\n", - " ERR162887\n", - " runs\n", - " ERS161286\n", + " 2021-07-23T09:20:39\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871389\n", " samples\n", - " 16279\n", - " 11515\n", - " 11515\n", - " 11515\n", - " 10046\n", + " ERR2098577\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1474912\n", + " 335618\n", + " 335618\n", + " 335618\n", + " 335119\n", " 0\n", " N/A\n", " N/A\n", @@ -679,26 +679,26 @@ " \n", " 1\n", " analysis-jobs\n", - " MGYA00646967\n", - " amplicon\n", - " MGYA00646967\n", + " MGYA00585243\n", + " MGYA00585243\n", " completed\n", + " amplicon\n", " 5.0\n", " False\n", - " 2023-07-19T14:11:59\n", - " LS454\n", - " 454 GS FLX\n", - " MGYS00006253\n", - " studies\n", - " ERR162901\n", - " runs\n", - " ERS161300\n", + " 2021-07-23T09:22:00\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871409\n", " samples\n", - " 9967\n", - " 9113\n", - " 9113\n", - " 9113\n", - " 8431\n", + " ERR2098437\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1178982\n", + " 1060738\n", + " 1060738\n", + " 1060738\n", + " 1049588\n", " 0\n", " N/A\n", " N/A\n", @@ -710,27 +710,27 @@ " \n", " 2\n", " analysis-jobs\n", - " MGYA00646970\n", - " amplicon\n", - " MGYA00646970\n", + " MGYA00585244\n", + " MGYA00585244\n", " completed\n", + " amplicon\n", " 5.0\n", " False\n", - " 2023-07-19T14:12:24\n", - " LS454\n", - " 454 GS FLX\n", - " MGYS00006253\n", - " studies\n", - " ERR162898\n", - " runs\n", - " ERS161297\n", + " 2021-07-23T09:24:06\n", + " ILLUMINA\n", + " Illumina MiSeq\n", + " ERS1871427\n", " samples\n", - " 8658\n", - " 7850\n", - " 7850\n", - " 7850\n", - " 7057\n", - " 0\n", + " ERR2098500\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 2285192\n", + " 772970\n", + " 772970\n", + " 772970\n", + " 772662\n", + " 3\n", " N/A\n", " N/A\n", " N/A\n", @@ -741,26 +741,26 @@ " \n", " 3\n", " analysis-jobs\n", - " MGYA00646973\n", - " amplicon\n", - " MGYA00646973\n", + " MGYA00585245\n", + " MGYA00585245\n", " completed\n", + " amplicon\n", " 5.0\n", " False\n", - " 2023-07-19T14:12:50\n", - " LS454\n", - " 454 GS FLX\n", - " MGYS00006253\n", - " studies\n", - " ERR162896\n", - " runs\n", - " ERS161295\n", + " 2021-07-23T09:25:40\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871411\n", " samples\n", - " 7008\n", - " 6632\n", - " 6632\n", - " 6632\n", - " 6556\n", + " ERR2098439\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1863283\n", + " 1679669\n", + " 1679669\n", + " 1679669\n", + " 1674242\n", " 0\n", " N/A\n", " N/A\n", @@ -772,26 +772,26 @@ " \n", " 4\n", " analysis-jobs\n", - " MGYA00646976\n", - " amplicon\n", - " MGYA00646976\n", + " MGYA00585246\n", + " MGYA00585246\n", " completed\n", + " amplicon\n", " 5.0\n", " False\n", - " 2023-07-19T14:13:10\n", - " LS454\n", - " 454 GS FLX\n", - " MGYS00006253\n", - " studies\n", - " ERR162895\n", - " runs\n", - " ERS161294\n", + " 2021-07-23T09:27:22\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871430\n", " samples\n", - " 8317\n", - " 7225\n", - " 7225\n", - " 7225\n", - " 6692\n", + " ERR2098450\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1244024\n", + " 1110539\n", + " 1110539\n", + " 1110539\n", + " 1107011\n", " 0\n", " N/A\n", " N/A\n", @@ -805,89 +805,89 @@ "" ], "text/plain": [ - " type id attributes.experiment-type \\\n", - "0 analysis-jobs MGYA00646965 amplicon \n", - "1 analysis-jobs MGYA00646967 amplicon \n", - "2 analysis-jobs MGYA00646970 amplicon \n", - "3 analysis-jobs MGYA00646973 amplicon \n", - "4 analysis-jobs MGYA00646976 amplicon \n", - "\n", - " attributes.accession attributes.analysis-status attributes.pipeline-version \\\n", - "0 MGYA00646965 completed 5.0 \n", - "1 MGYA00646967 completed 5.0 \n", - "2 MGYA00646970 completed 5.0 \n", - "3 MGYA00646973 completed 5.0 \n", - "4 MGYA00646976 completed 5.0 \n", - "\n", - " attributes.is-private attributes.complete-time \\\n", - "0 False 2023-07-19T14:11:39 \n", - "1 False 2023-07-19T14:11:59 \n", - "2 False 2023-07-19T14:12:24 \n", - "3 False 2023-07-19T14:12:50 \n", - "4 False 2023-07-19T14:13:10 \n", + " type id attributes.accession \\\n", + "0 analysis-jobs MGYA00585242 MGYA00585242 \n", + "1 analysis-jobs MGYA00585243 MGYA00585243 \n", + "2 analysis-jobs MGYA00585244 MGYA00585244 \n", + "3 analysis-jobs MGYA00585245 MGYA00585245 \n", + "4 analysis-jobs MGYA00585246 MGYA00585246 \n", + "\n", + " attributes.analysis-status attributes.experiment-type \\\n", + "0 completed amplicon \n", + "1 completed amplicon \n", + "2 completed amplicon \n", + "3 completed amplicon \n", + "4 completed amplicon \n", + "\n", + " attributes.pipeline-version attributes.is-private attributes.complete-time \\\n", + "0 5.0 False 2021-07-23T09:20:39 \n", + "1 5.0 False 2021-07-23T09:22:00 \n", + "2 5.0 False 2021-07-23T09:24:06 \n", + "3 5.0 False 2021-07-23T09:25:40 \n", + "4 5.0 False 2021-07-23T09:27:22 \n", "\n", " attributes.instrument-platform attributes.instrument-model \\\n", - "0 LS454 454 GS FLX \n", - "1 LS454 454 GS FLX \n", - "2 LS454 454 GS FLX \n", - "3 LS454 454 GS FLX \n", - "4 LS454 454 GS FLX \n", + "0 ILLUMINA Illumina HiSeq 2500 \n", + "1 ILLUMINA Illumina HiSeq 2500 \n", + "2 ILLUMINA Illumina MiSeq \n", + "3 ILLUMINA Illumina HiSeq 2500 \n", + "4 ILLUMINA Illumina HiSeq 2500 \n", "\n", - " relationships.study.data.id relationships.study.data.type \\\n", - "0 MGYS00006253 studies \n", - "1 MGYS00006253 studies \n", - "2 MGYS00006253 studies \n", - "3 MGYS00006253 studies \n", - "4 MGYS00006253 studies \n", + " relationships.sample.data.id relationships.sample.data.type \\\n", + "0 ERS1871389 samples \n", + "1 ERS1871409 samples \n", + "2 ERS1871427 samples \n", + "3 ERS1871411 samples \n", + "4 ERS1871430 samples \n", "\n", " relationships.run.data.id relationships.run.data.type \\\n", - "0 ERR162887 runs \n", - "1 ERR162901 runs \n", - "2 ERR162898 runs \n", - "3 ERR162896 runs \n", - "4 ERR162895 runs \n", + "0 ERR2098577 runs \n", + "1 ERR2098437 runs \n", + "2 ERR2098500 runs \n", + "3 ERR2098439 runs \n", + "4 ERR2098450 runs \n", "\n", - " relationships.sample.data.id relationships.sample.data.type \\\n", - "0 ERS161286 samples \n", - "1 ERS161300 samples \n", - "2 ERS161297 samples \n", - "3 ERS161295 samples \n", - "4 ERS161294 samples \n", + " relationships.study.data.id relationships.study.data.type \\\n", + "0 MGYS00001935 studies \n", + "1 MGYS00001935 studies \n", + "2 MGYS00001935 studies \n", + "3 MGYS00001935 studies \n", + "4 MGYS00001935 studies \n", "\n", " Submitted nucleotide sequences \\\n", - "0 16279 \n", - "1 9967 \n", - "2 8658 \n", - "3 7008 \n", - "4 8317 \n", + "0 1474912 \n", + "1 1178982 \n", + "2 2285192 \n", + "3 1863283 \n", + "4 1244024 \n", "\n", " Nucleotide sequences after format-specific filtering \\\n", - "0 11515 \n", - "1 9113 \n", - "2 7850 \n", - "3 6632 \n", - "4 7225 \n", + "0 335618 \n", + "1 1060738 \n", + "2 772970 \n", + "3 1679669 \n", + "4 1110539 \n", "\n", " Nucleotide sequences after length filtering \\\n", - "0 11515 \n", - "1 9113 \n", - "2 7850 \n", - "3 6632 \n", - "4 7225 \n", + "0 335618 \n", + "1 1060738 \n", + "2 772970 \n", + "3 1679669 \n", + "4 1110539 \n", "\n", " Nucleotide sequences after undetermined bases filtering \\\n", - "0 11515 \n", - "1 9113 \n", - "2 7850 \n", - "3 6632 \n", - "4 7225 \n", + "0 335618 \n", + "1 1060738 \n", + "2 772970 \n", + "3 1679669 \n", + "4 1110539 \n", "\n", " Predicted SSU sequences Predicted LSU sequences Reads with predicted CDS \\\n", - "0 10046 0 N/A \n", - "1 8431 0 N/A \n", - "2 7057 0 N/A \n", - "3 6556 0 N/A \n", - "4 6692 0 N/A \n", + "0 335119 0 N/A \n", + "1 1049588 0 N/A \n", + "2 772662 3 N/A \n", + "3 1674242 0 N/A \n", + "4 1107011 0 N/A \n", "\n", " Reads with predicted RNA Reads with InterProScan match Predicted CDS \\\n", "0 N/A N/A N/A \n", @@ -904,7 +904,7 @@ "4 N/A N/A " ] }, - "execution_count": 247, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -961,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 8, "id": "e1a219d6-26e5-4e7e-8c6c-571cb32ea9d6", "metadata": { "tags": [] @@ -992,8 +992,8 @@ " id\n", " attributes.accession\n", " attributes.analysis-status\n", - " attributes.pipeline-version\n", " attributes.experiment-type\n", + " attributes.pipeline-version\n", " attributes.is-private\n", " attributes.complete-time\n", " attributes.instrument-platform\n", @@ -1019,17 +1019,539 @@ " \n", " \n", " \n", + " \n", + " 2\n", + " analysis-jobs\n", + " MGYA00585244\n", + " MGYA00585244\n", + " completed\n", + " amplicon\n", + " 5.0\n", + " False\n", + " 2021-07-23T09:24:06\n", + " ILLUMINA\n", + " Illumina MiSeq\n", + " ERS1871427\n", + " samples\n", + " ERR2098500\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 2285192\n", + " 772970\n", + " 772970\n", + " 772970\n", + " 772662\n", + " 3\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " \n", + " \n", + " 12\n", + " analysis-jobs\n", + " MGYA00585254\n", + " MGYA00585254\n", + " completed\n", + " amplicon\n", + " 5.0\n", + " False\n", + " 2021-07-23T09:39:02\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871398\n", + " samples\n", + " ERR2098533\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 994953\n", + " 757022\n", + " 757022\n", + " 757022\n", + " 755379\n", + " 3\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " \n", + " \n", + " 13\n", + " analysis-jobs\n", + " MGYA00585255\n", + " MGYA00585255\n", + " completed\n", + " amplicon\n", + " 5.0\n", + " False\n", + " 2021-07-23T09:40:50\n", + " ILLUMINA\n", + " Illumina MiSeq\n", + " ERS1996201\n", + " samples\n", + " ERR2196988\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 670174\n", + " 478699\n", + " 478699\n", + " 478699\n", + " 472895\n", + " 5\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " \n", + " \n", + " 17\n", + " analysis-jobs\n", + " MGYA00585259\n", + " MGYA00585259\n", + " completed\n", + " amplicon\n", + " 5.0\n", + " False\n", + " 2021-07-23T14:15:40\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871471\n", + " samples\n", + " ERR2098506\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1323302\n", + " 765789\n", + " 765789\n", + " 765789\n", + " 764645\n", + " 5\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " \n", + " \n", + " 21\n", + " analysis-jobs\n", + " MGYA00585263\n", + " MGYA00585263\n", + " completed\n", + " amplicon\n", + " 5.0\n", + " False\n", + " 2021-07-23T14:22:58\n", + " ILLUMINA\n", + " Illumina HiSeq 2500\n", + " ERS1871374\n", + " samples\n", + " ERR2098570\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 1110577\n", + " 590505\n", + " 590505\n", + " 590505\n", + " 589230\n", + " 1\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " N/A\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 243\n", + " analysis-jobs\n", + " MGYA00608340\n", + " MGYA00608340\n", + " completed\n", + " metagenomic\n", + " 5.0\n", + " False\n", + " 2022-09-20T11:01:47\n", + " ILLUMINA\n", + " Illumina HiSeq 4000\n", + " ERS1871393\n", + " samples\n", + " ERR2098379\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 62248568\n", + " 25722964\n", + " 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ERS1871374\n", + " samples\n", + " ERR2098368\n", + " runs\n", + " MGYS00001935\n", + " studies\n", + " 62556828\n", + " 32656692\n", + " 32656692\n", + " 32656692\n", + " 30276\n", + " 53113\n", + " 31171698\n", + " 107374\n", + " 13136590\n", + " 31875576\n", + " 13171653\n", + " 22950127\n", + " \n", " \n", "\n", + "

108 rows × 28 columns

\n", "" ], "text/plain": [ - "Empty DataFrame\n", - "Columns: [type, id, attributes.accession, attributes.analysis-status, attributes.pipeline-version, attributes.experiment-type, attributes.is-private, attributes.complete-time, attributes.instrument-platform, attributes.instrument-model, relationships.sample.data.id, relationships.sample.data.type, relationships.run.data.id, relationships.run.data.type, relationships.study.data.id, relationships.study.data.type, Submitted nucleotide sequences, Nucleotide sequences after format-specific filtering, Nucleotide sequences after length filtering, Nucleotide sequences after undetermined bases filtering, Predicted SSU sequences, Predicted LSU sequences, Reads with predicted CDS, Reads with predicted RNA, Reads with InterProScan match, Predicted CDS, Predicted CDS with InterProScan match, Total InterProScan matches]\n", - "Index: []" + " type id attributes.accession \\\n", + "2 analysis-jobs MGYA00585244 MGYA00585244 \n", + "12 analysis-jobs MGYA00585254 MGYA00585254 \n", + "13 analysis-jobs MGYA00585255 MGYA00585255 \n", + "17 analysis-jobs MGYA00585259 MGYA00585259 \n", + "21 analysis-jobs MGYA00585263 MGYA00585263 \n", + ".. ... ... ... \n", + "243 analysis-jobs MGYA00608340 MGYA00608340 \n", + "244 analysis-jobs MGYA00608341 MGYA00608341 \n", + "245 analysis-jobs MGYA00608342 MGYA00608342 \n", + "246 analysis-jobs MGYA00608343 MGYA00608343 \n", + "247 analysis-jobs MGYA00608344 MGYA00608344 \n", + "\n", + " attributes.analysis-status attributes.experiment-type \\\n", + "2 completed amplicon \n", + "12 completed amplicon \n", + "13 completed amplicon \n", + "17 completed amplicon \n", + "21 completed amplicon \n", + ".. ... ... \n", + "243 completed metagenomic \n", + "244 completed metagenomic \n", + "245 completed metagenomic \n", + "246 completed metagenomic \n", + "247 completed metagenomic \n", + "\n", + " attributes.pipeline-version attributes.is-private \\\n", + "2 5.0 False \n", + "12 5.0 False \n", + "13 5.0 False \n", + "17 5.0 False \n", + "21 5.0 False \n", + ".. ... ... \n", + "243 5.0 False \n", + "244 5.0 False \n", + "245 5.0 False \n", + "246 5.0 False \n", + "247 5.0 False \n", + "\n", + " attributes.complete-time attributes.instrument-platform \\\n", + "2 2021-07-23T09:24:06 ILLUMINA \n", + "12 2021-07-23T09:39:02 ILLUMINA \n", + "13 2021-07-23T09:40:50 ILLUMINA \n", + "17 2021-07-23T14:15:40 ILLUMINA \n", + "21 2021-07-23T14:22:58 ILLUMINA \n", + ".. ... ... \n", + "243 2022-09-20T11:01:47 ILLUMINA \n", + "244 2022-09-20T11:03:41 ILLUMINA \n", + "245 2022-09-20T11:05:20 ILLUMINA \n", + "246 2022-09-20T11:07:02 ILLUMINA \n", + "247 2022-09-20T11:08:51 ILLUMINA \n", + "\n", + " attributes.instrument-model relationships.sample.data.id \\\n", + "2 Illumina MiSeq ERS1871427 \n", + "12 Illumina HiSeq 2500 ERS1871398 \n", + "13 Illumina MiSeq ERS1996201 \n", + "17 Illumina HiSeq 2500 ERS1871471 \n", + "21 Illumina HiSeq 2500 ERS1871374 \n", + ".. ... ... \n", + "243 Illumina HiSeq 4000 ERS1871393 \n", + "244 Illumina HiSeq 4000 ERS1871423 \n", + "245 Illumina HiSeq 4000 ERS1871373 \n", + "246 Illumina HiSeq 4000 ERS1871401 \n", + "247 Illumina HiSeq 4000 ERS1871374 \n", + "\n", + " relationships.sample.data.type relationships.run.data.id \\\n", + "2 samples ERR2098500 \n", + "12 samples ERR2098533 \n", + "13 samples ERR2196988 \n", + "17 samples ERR2098506 \n", + "21 samples ERR2098570 \n", + ".. ... ... \n", + "243 samples ERR2098379 \n", + "244 samples ERR2098403 \n", + "245 samples ERR2098367 \n", + "246 samples ERR2098385 \n", + "247 samples ERR2098368 \n", + "\n", + " relationships.run.data.type relationships.study.data.id \\\n", + "2 runs MGYS00001935 \n", + "12 runs MGYS00001935 \n", + "13 runs MGYS00001935 \n", + "17 runs MGYS00001935 \n", + "21 runs MGYS00001935 \n", + ".. ... ... \n", + "243 runs MGYS00001935 \n", + "244 runs MGYS00001935 \n", + "245 runs MGYS00001935 \n", + "246 runs MGYS00001935 \n", + "247 runs MGYS00001935 \n", + "\n", + " relationships.study.data.type Submitted nucleotide sequences \\\n", + "2 studies 2285192 \n", + "12 studies 994953 \n", + "13 studies 670174 \n", + "17 studies 1323302 \n", + "21 studies 1110577 \n", + ".. ... ... \n", + "243 studies 62248568 \n", + "244 studies 64768750 \n", + "245 studies 51217703 \n", + "246 studies 49633529 \n", + "247 studies 62556828 \n", + "\n", + " Nucleotide sequences after format-specific filtering \\\n", + "2 772970 \n", + "12 757022 \n", + "13 478699 \n", + "17 765789 \n", + "21 590505 \n", + ".. ... \n", + "243 25722964 \n", + "244 30469803 \n", + "245 24766186 \n", + "246 23913778 \n", + "247 32656692 \n", + "\n", + " Nucleotide sequences after length filtering \\\n", + "2 772970 \n", + "12 757022 \n", + "13 478699 \n", + "17 765789 \n", + "21 590505 \n", + ".. ... \n", + "243 25722964 \n", + "244 30469803 \n", + "245 24766186 \n", + "246 23913778 \n", + "247 32656692 \n", + "\n", + " Nucleotide sequences after undetermined bases filtering \\\n", + "2 772970 \n", + "12 757022 \n", + "13 478699 \n", + "17 765789 \n", + "21 590505 \n", + ".. ... \n", + "243 25722964 \n", + "244 30469803 \n", + "245 24766186 \n", + "246 23913778 \n", + "247 32656692 \n", + "\n", + " Predicted SSU sequences Predicted LSU sequences Reads with predicted CDS \\\n", + "2 772662 3 N/A \n", + "12 755379 3 N/A \n", + "13 472895 5 N/A \n", + "17 764645 5 N/A \n", + "21 589230 1 N/A \n", + ".. ... ... ... \n", + "243 21509 39264 25040753 \n", + "244 18363 31760 28024481 \n", + "245 21370 38041 23518432 \n", + "246 34631 52285 19997664 \n", + "247 30276 53113 31171698 \n", + "\n", + " Reads with predicted RNA Reads with InterProScan match Predicted CDS \\\n", + "2 N/A N/A N/A \n", + "12 N/A N/A N/A \n", + "13 N/A N/A N/A \n", + "17 N/A N/A N/A \n", + "21 N/A N/A N/A \n", + ".. ... ... ... \n", + "243 83388 12776564 25730026 \n", + "244 62320 6244999 28646835 \n", + "245 76618 9443549 24051747 \n", + "246 98831 2645285 20608046 \n", + "247 107374 13136590 31875576 \n", + "\n", + " Predicted CDS with InterProScan match Total InterProScan matches \n", + "2 N/A N/A \n", + "12 N/A N/A \n", + "13 N/A N/A \n", + "17 N/A N/A \n", + "21 N/A N/A \n", + ".. ... ... \n", + "243 12822303 22748978 \n", + "244 6258524 10588518 \n", + "245 9468683 16474824 \n", + "246 2649968 4440764 \n", + "247 13171653 22950127 \n", + "\n", + "[108 rows x 28 columns]" ] }, - "execution_count": 230, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1061,7 +1583,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 9, "id": "6e0210f7-d788-48a7-b327-2763b8183bbb", "metadata": { "tags": [] @@ -1070,10 +1592,10 @@ { "data": { "text/plain": [ - "0" + "108" ] }, - "execution_count": 231, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1105,14 +1627,14 @@ "tags": [] }, "source": [ - "Now let's fetch the actual results files. To do that we will use the ```/analyses/{accession}/downloads``` endpoint. For the purposes of this exercise we will download the results for only the first 2 analyses from the unfiltered dataframe ```analyses``` but in your own work you could download them all or download only the ones that passed the filters that you applied. \n", + "Now let's fetch the actual results files. To do that we will use the ```/analyses/{analysisId}/downloads``` endpoint. For the purposes of this exercise we will download the results for only the first 2 analyses from the unfiltered dataframe ```analyses``` but in your own work you could download them all or download only the ones that passed the filters that you applied. \n", "\n", "There are different result files available, let's first check which ones we could download:" ] }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 11, "id": "269794af-c4c8-49c3-a832-8c0ce8a5241b", "metadata": { "tags": [] @@ -1123,31 +1645,29 @@ "output_type": "stream", "text": [ "\n", - "Files available for analysis MGYA00646965:\n", - "ERR162901_FASTQ.fasta.gz: Processed nucleotide reads\n", - "ERR162901_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", - "ERR162901_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", - "ERR162901_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR162901_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR162901_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "Files available for analysis MGYA00585242:\n", + "ERR2098577_MERGED_FASTQ.fasta.gz: Processed nucleotide reads\n", + "ERR2098577_MERGED_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", + "ERR2098577_MERGED_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", + "ERR2098577_MERGED_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR2098577_MERGED_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR2098577_MERGED_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", "\n", - "Files available for analysis MGYA00646967:\n", - "ERR162901_FASTQ.fasta.gz: Processed nucleotide reads\n", - "ERR162901_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", - "ERR162901_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", - "ERR162901_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR162901_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR162901_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n" + "Files available for analysis MGYA00585243:\n", + "ERR2098437_MERGED_FASTQ.fasta.gz: Processed nucleotide reads\n", + "ERR2098437_MERGED_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", + "ERR2098437_MERGED_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", + "ERR2098437_MERGED_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR2098437_MERGED_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", + "ERR2098437_MERGED_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n" ] } ], "source": [ - "endpoint = f\"analyses/{analysisId}/downloads\"\n", - "\n", "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session:\n", " for analysisId in analyses.head(2)['attributes.accession']:\n", " print(f\"\\nFiles available for analysis {analysisId}:\")\n", - " for download in session.iterate(endpoint):\n", + " for download in session.iterate(f\"analyses/{analysisId}/downloads\"):\n", " print(f\"{download.alias}: {download.description.label}\")" ] }, @@ -1161,25 +1681,12 @@ }, { "cell_type": "code", - "execution_count": 373, + "execution_count": null, "id": "aef7bd3e-79d5-44cf-ba47-a8e6259c21d6", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: MGYA00646965\n", - "Downloading file for MGYA00646965\n", - "Finished for MGYA00646965\n", - "Processing: MGYA00646967\n", - "Downloading file for MGYA00646967\n", - "Finished for MGYA00646967\n" - ] - } - ], + "outputs": [], "source": [ "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session: \n", " for analysisId in analyses.head(2)['attributes.accession']:\n", @@ -1210,7 +1717,7 @@ }, { "cell_type": "code", - "execution_count": 259, + "execution_count": null, "id": "8d40a77f-fd58-4d9b-a5f9-95edbc49dcbe", "metadata": { "jupyter": { @@ -1218,20 +1725,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: MGYA00646965\n", - "Downloading file for MGYA00646965\n", - "Finished for MGYA00646965\n", - "Processing: MGYA00646967\n", - "Downloading file for MGYA00646967\n", - "Finished for MGYA00646967\n" - ] - } - ], + "outputs": [], "source": [ "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session:\n", " # Starting a connection to MGnify's API just like we did before\n", @@ -1321,7 +1815,7 @@ }, { "cell_type": "code", - "execution_count": 355, + "execution_count": null, "id": "5277dccd-35b4-43b4-b53c-cd23ce655222", "metadata": { "tags": [] @@ -1341,70 +1835,12 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": null, "id": "967be7ab-ec02-4759-a09c-e383ab874769", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'genomes',\n", - " 'id': 'MGYG000321626',\n", - " 'attributes': {'genome-id': 17285,\n", - " 'geographic-range': [],\n", - " 'geographic-origin': 'Europe',\n", - " 'accession': 'MGYG000321626',\n", - " 'ena-genome-accession': None,\n", - " 'ena-sample-accession': 'SAMN33097420',\n", - " 'ena-study-accession': None,\n", - " 'ncbi-genome-accession': 'GCA_028655675',\n", - " 'ncbi-sample-accession': None,\n", - " 'ncbi-study-accession': 'PRJNA931735',\n", - " 'img-genome-accession': None,\n", - " 'patric-genome-accession': None,\n", - " 'length': 1881475,\n", - " 'num-contigs': 145,\n", - " 'n-50': 32562,\n", - " 'gc-content': 49.81,\n", - " 'type': 'Isolate',\n", - " 'completeness': 99.03,\n", - " 'contamination': 0.0,\n", - " 'rna-5s': 93.28,\n", - " 'rna-16s': 99.41,\n", - " 'rna-23s': 97.64,\n", - " 'trnas': 20.0,\n", - " 'nc-rnas': 26,\n", - " 'num-proteins': 1803,\n", - " 'eggnog-coverage': 95.73,\n", - " 'ipr-coverage': 89.24,\n", - " 'taxon-lineage': 'd__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii',\n", - " 'num-genomes-total': 1,\n", - " 'pangenome-size': None,\n", - " 'pangenome-core-size': None,\n", - " 'pangenome-accessory-size': None,\n", - " 'last-update': '2023-07-07T11:35:45.488929',\n", - " 'first-created': '2023-07-07T11:35:45.486483'},\n", - " 'relationships': {'antismash-geneclusters': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/antismash-genecluster'}},\n", - " 'kegg-class-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/kegg-class'}},\n", - " 'catalogue': {'data': {'type': 'genome-catalogues',\n", - " 'id': 'honeybee-gut-v1-0'},\n", - " 'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genome-catalogues/honeybee-gut-v1-0'}},\n", - " 'downloads': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/downloads'}},\n", - " 'cog-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/cogs'}},\n", - " 'kegg-modules-matches': {'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626/kegg-module'}},\n", - " 'biome': {'data': {'type': 'biomes',\n", - " 'id': 'root:Host-associated:Insecta:Digestive system'},\n", - " 'links': {'related': 'https://www.ebi.ac.uk/metagenomics/api/v1/biomes/root:Host-associated:Insecta:Digestive%20system'}}},\n", - " 'links': {'self': 'https://www.ebi.ac.uk/metagenomics/api/v1/genomes/MGYG000321626'}}" - ] - }, - "execution_count": 263, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "r = requests.get(f\"https://www.ebi.ac.uk/metagenomics/api/v1/{endpoint}\")\n", "r.json()['data'][0]" @@ -1468,7 +1904,7 @@ }, { "cell_type": "code", - "execution_count": 356, + "execution_count": null, "id": "ae24320a-813b-4658-bb09-63313e8195bb", "metadata": { "tags": [] @@ -1489,7 +1925,7 @@ }, { "cell_type": "code", - "execution_count": 357, + "execution_count": null, "id": "ddb1d36f-a3a4-4d25-85a7-3b68281ccdf4", "metadata": { "tags": [] @@ -1510,219 +1946,12 @@ }, { "cell_type": "code", - "execution_count": 358, + "execution_count": null, "id": "16da3b82-4bd3-4043-b854-6da4bf39c149", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ena-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.last-updateattributes.first-createdrelationships.catalogue.data.idrelationships.catalogue.data.typerelationships.biome.data.idrelationships.biome.data.type
0genomesMGYG00029920713928Europe[North America, Europe]MGYG000299207ERS7621657ERP12511730444751015361340.71MAG99.320.060.00.00.019.020240588.8184.12d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica37504578142642021-12-17T10:56:55.4213212021-12-15T12:21:00.266046human-oral-v1-0genome-cataloguesroot:Host-associated:Human:Digestive system:Oralbiomes
1genomesMGYG0000032877834North America[North America, Europe, Asia]MGYG000003287SRS147088SRP00216332592431455718940.52MAG96.850.340.00.00.019.023272187.0379.64d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica4252064318772021-12-07T19:41:43.4562272021-12-07T19:41:43.453627human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomes
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" - ], - "text/plain": [ - " type id attributes.genome-id attributes.geographic-origin \\\n", - "0 genomes MGYG000299207 13928 Europe \n", - "1 genomes MGYG000003287 7834 North America \n", - "\n", - " attributes.geographic-range attributes.accession \\\n", - "0 [North America, Europe] MGYG000299207 \n", - "1 [North America, Europe, Asia] MGYG000003287 \n", - "\n", - " attributes.ena-sample-accession attributes.ena-study-accession \\\n", - "0 ERS7621657 ERP125117 \n", - "1 SRS147088 SRP002163 \n", - "\n", - " attributes.length attributes.num-contigs attributes.n-50 \\\n", - "0 3044475 101 53613 \n", - "1 3259243 145 57189 \n", - "\n", - " attributes.gc-content attributes.type attributes.completeness \\\n", - "0 40.71 MAG 99.32 \n", - "1 40.52 MAG 96.85 \n", - "\n", - " attributes.contamination attributes.rna-5s attributes.rna-16s \\\n", - "0 0.06 0.0 0.0 \n", - "1 0.34 0.0 0.0 \n", - "\n", - " attributes.rna-23s attributes.trnas attributes.nc-rnas \\\n", - "0 0.0 19.0 20 \n", - "1 0.0 19.0 23 \n", - "\n", - " attributes.num-proteins attributes.eggnog-coverage \\\n", - "0 2405 88.81 \n", - "1 2721 87.03 \n", - "\n", - " attributes.ipr-coverage \\\n", - "0 84.12 \n", - "1 79.64 \n", - "\n", - " attributes.taxon-lineage \\\n", - "0 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica \n", - "1 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella melaninogenica \n", - "\n", - " attributes.num-genomes-total attributes.pangenome-size \\\n", - "0 37 5045 \n", - "1 4 2520 \n", - "\n", - " attributes.pangenome-core-size attributes.pangenome-accessory-size \\\n", - "0 781 4264 \n", - "1 643 1877 \n", - "\n", - " attributes.last-update attributes.first-created \\\n", - "0 2021-12-17T10:56:55.421321 2021-12-15T12:21:00.266046 \n", - "1 2021-12-07T19:41:43.456227 2021-12-07T19:41:43.453627 \n", - "\n", - " relationships.catalogue.data.id relationships.catalogue.data.type \\\n", - "0 human-oral-v1-0 genome-catalogues \n", - "1 human-gut-v2-0-1 genome-catalogues \n", - "\n", - " relationships.biome.data.id \\\n", - "0 root:Host-associated:Human:Digestive system:Oral \n", - "1 root:Host-associated:Human:Digestive system:Large intestine \n", - "\n", - " relationships.biome.data.type \n", - "0 biomes \n", - "1 biomes " - ] - }, - "execution_count": 358, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Display the table containing the results of the query\n", "resources_df_sp" @@ -1748,7 +1977,7 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": null, "id": "491f9888-2bef-4823-8e02-4e74c4a908af", "metadata": { "tags": [] @@ -1763,761 +1992,12 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": null, "id": "568c1673-91bb-4a7b-b302-308e774bc127", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ena-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.last-updateattributes.first-createdrelationships.catalogue.data.idrelationships.catalogue.data.typerelationships.biome.data.idrelationships.biome.data.typeattributes.ncbi-genome-accessionattributes.ncbi-sample-accessionattributes.ncbi-study-accession
0genomesMGYG00031896717013Europe[Europe]MGYG000318967SAMEA112352570ERP1250743118135637714451.72MAG98.8800000.6800000.000.000.0017.018255789.2184.90d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercoripullorum62717.0803.01914.02023-04-26T10:45:44.1593872023-04-26T10:45:44.156146chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
1genomesMGYG00031751316001Europe[Europe]MGYG000317513SAMEA112351745ERP12507430717754410127647.79MAG98.7000000.1800000.0020.090.0018.023258988.4983.62d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp000433175313656.01064.02592.02023-04-26T10:12:21.2221942023-04-26T10:12:21.219735chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
2genomesMGYG00031737916261Europe[]MGYG000317379SAMEA112348875ERP1250742443261384717148.85MAG80.5300000.8300000.000.000.0010.09199380.6879.13d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella lascolaii1NaNNaNNaN2023-04-26T10:20:51.0416292023-04-26T10:20:51.039628chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesNaNNaNNaN
3genomesMGYG00030820117014not provided[]MGYG000308201SAMN15816872SRP1993072869782383939556.37MAG85.1594651.6666670.000.000.0015.014219088.4587.12d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp9005404151NaNNaNNaN2023-04-26T10:45:46.2557062023-04-26T10:45:46.252869chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesGCA_019113095NaNNaN
4genomesMGYG00030802015777not provided[]MGYG000308020SAMN15816846SRP1993072294566734426051.33MAG96.1148651.35135194.120.0010.5620.021194092.2789.95d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella avicola1NaNNaNNaN2023-04-26T10:05:08.4225462023-04-26T10:05:08.418890chicken-gut-v1-0genome-cataloguesroot:Host-associated:Birds:Digestive system:Digestive tubebiomesGCA_019116685NaNNaN
..................................................................................................................
443genomesMGYG0000005346592Oceania[North America, Europe, Asia, Oceania, South America, Africa]MGYG000000534SRS476185SRP0294413142227855349349.62MAG96.8600000.0500000.000.000.0019.020245188.0084.94d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp9005439753107124.0872.06252.02021-12-07T19:07:36.4353372021-12-07T19:07:36.432779human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
444genomesMGYG0000005265736Oceania[Europe, Oceania]MGYG000000526SRS476306SRP02944135100353818253647.78MAG97.7800000.1900000.000.000.0019.017288091.1187.60d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp90077039543119.01696.01423.02021-12-07T18:38:44.0853582021-12-07T18:38:44.061293human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
445genomesMGYG0000002895013Europe[North America, Europe, Asia]MGYG000000289ERS473073ERP0059893001000845235051.55MAG98.4700000.00000094.120.0011.2518.021242788.6385.54d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella buccae745042.0500.04542.02021-12-07T18:12:03.2869752021-12-07T18:12:03.284256human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNNaNNaN
446genomesMGYG0000002728133Asia[North America, Europe, Asia, Oceania, South America, Africa]MGYG000000272NaNNaN39910642424882643.02Isolate98.1400001.52000094.9699.6799.5620.036337087.6981.66d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis16217364.0525.016839.02021-12-07T19:49:53.9571292021-12-07T19:49:53.954642human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNSAMN09736551PRJNA482748
447genomesMGYG0000002159337Asia[North America, Europe, Asia, Oceania, South America, Africa, NA]MGYG000000215NaNNaN31176481524005848.90Isolate97.9000000.00000095.8099.6799.4520.035286789.6483.26d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercorea4059388.0567.08821.02021-12-07T20:23:55.4434622021-12-07T20:23:55.440610human-gut-v2-0-1genome-cataloguesroot:Host-associated:Human:Digestive system:Large intestinebiomesNaNSAMN09734707PRJNA482748
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448 rows × 37 columns

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" - ], - "text/plain": [ - " type id attributes.genome-id \\\n", - "0 genomes MGYG000318967 17013 \n", - "1 genomes MGYG000317513 16001 \n", - "2 genomes MGYG000317379 16261 \n", - "3 genomes MGYG000308201 17014 \n", - "4 genomes MGYG000308020 15777 \n", - ".. ... ... ... \n", - "443 genomes MGYG000000534 6592 \n", - "444 genomes MGYG000000526 5736 \n", - "445 genomes MGYG000000289 5013 \n", - "446 genomes MGYG000000272 8133 \n", - "447 genomes MGYG000000215 9337 \n", - "\n", - " attributes.geographic-origin \\\n", - "0 Europe \n", - "1 Europe \n", - "2 Europe \n", - "3 not provided \n", - "4 not provided \n", - ".. ... \n", - "443 Oceania \n", - "444 Oceania \n", - "445 Europe \n", - "446 Asia \n", - "447 Asia \n", - "\n", - " attributes.geographic-range \\\n", - "0 [Europe] \n", - "1 [Europe] \n", - "2 [] \n", - "3 [] \n", - "4 [] \n", - ".. ... \n", - "443 [North America, Europe, Asia, Oceania, South America, Africa] \n", - "444 [Europe, Oceania] \n", - "445 [North America, Europe, Asia] \n", - "446 [North America, Europe, Asia, Oceania, South America, Africa] \n", - "447 [North America, Europe, Asia, Oceania, South America, Africa, NA] \n", - "\n", - " attributes.accession attributes.ena-sample-accession \\\n", - "0 MGYG000318967 SAMEA112352570 \n", - "1 MGYG000317513 SAMEA112351745 \n", - "2 MGYG000317379 SAMEA112348875 \n", - "3 MGYG000308201 SAMN15816872 \n", - "4 MGYG000308020 SAMN15816846 \n", - ".. ... ... \n", - "443 MGYG000000534 SRS476185 \n", - "444 MGYG000000526 SRS476306 \n", - "445 MGYG000000289 ERS473073 \n", - "446 MGYG000000272 NaN \n", - "447 MGYG000000215 NaN \n", - "\n", - " attributes.ena-study-accession attributes.length attributes.num-contigs \\\n", - "0 ERP125074 3118135 63 \n", - "1 ERP125074 3071775 44 \n", - "2 ERP125074 2443261 384 \n", - "3 SRP199307 2869782 383 \n", - "4 SRP199307 2294566 73 \n", - ".. ... ... ... \n", - "443 SRP029441 3142227 85 \n", - "444 SRP029441 3510035 38 \n", - "445 ERP005989 3001000 84 \n", - "446 NaN 3991064 242 \n", - "447 NaN 3117648 152 \n", - "\n", - " attributes.n-50 attributes.gc-content attributes.type \\\n", - "0 77144 51.72 MAG \n", - "1 101276 47.79 MAG \n", - "2 7171 48.85 MAG \n", - "3 9395 56.37 MAG \n", - "4 44260 51.33 MAG \n", - ".. ... ... ... \n", - "443 53493 49.62 MAG \n", - "444 182536 47.78 MAG \n", - "445 52350 51.55 MAG \n", - "446 48826 43.02 Isolate \n", - "447 40058 48.90 Isolate \n", - "\n", - " attributes.completeness attributes.contamination attributes.rna-5s \\\n", - "0 98.880000 0.680000 0.00 \n", - "1 98.700000 0.180000 0.00 \n", - "2 80.530000 0.830000 0.00 \n", - "3 85.159465 1.666667 0.00 \n", - "4 96.114865 1.351351 94.12 \n", - ".. ... ... ... \n", - "443 96.860000 0.050000 0.00 \n", - "444 97.780000 0.190000 0.00 \n", - "445 98.470000 0.000000 94.12 \n", - "446 98.140000 1.520000 94.96 \n", - "447 97.900000 0.000000 95.80 \n", - "\n", - " attributes.rna-16s attributes.rna-23s attributes.trnas \\\n", - "0 0.00 0.00 17.0 \n", - "1 20.09 0.00 18.0 \n", - "2 0.00 0.00 10.0 \n", - "3 0.00 0.00 15.0 \n", - "4 0.00 10.56 20.0 \n", - ".. ... ... ... \n", - "443 0.00 0.00 19.0 \n", - "444 0.00 0.00 19.0 \n", - "445 0.00 11.25 18.0 \n", - "446 99.67 99.56 20.0 \n", - "447 99.67 99.45 20.0 \n", - "\n", - " attributes.nc-rnas attributes.num-proteins attributes.eggnog-coverage \\\n", - "0 18 2557 89.21 \n", - "1 23 2589 88.49 \n", - "2 9 1993 80.68 \n", - "3 14 2190 88.45 \n", - "4 21 1940 92.27 \n", - ".. ... ... ... \n", - "443 20 2451 88.00 \n", - "444 17 2880 91.11 \n", - "445 21 2427 88.63 \n", - "446 36 3370 87.69 \n", - "447 35 2867 89.64 \n", - "\n", - " attributes.ipr-coverage \\\n", - "0 84.90 \n", - "1 83.62 \n", - "2 79.13 \n", - "3 87.12 \n", - "4 89.95 \n", - ".. ... \n", - "443 84.94 \n", - "444 87.60 \n", - "445 85.54 \n", - "446 81.66 \n", - "447 83.26 \n", - "\n", - " attributes.taxon-lineage \\\n", - "0 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercoripullorum \n", - "1 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp000433175 \n", - "2 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella lascolaii \n", - "3 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900540415 \n", - "4 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella avicola \n", - ".. ... \n", - "443 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900543975 \n", - "444 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900770395 \n", - "445 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella buccae \n", - "446 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis \n", - "447 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella stercorea \n", - "\n", - " attributes.num-genomes-total attributes.pangenome-size \\\n", - "0 6 2717.0 \n", - "1 31 3656.0 \n", - "2 1 NaN \n", - "3 1 NaN \n", - "4 1 NaN \n", - ".. ... ... \n", - "443 310 7124.0 \n", - "444 4 3119.0 \n", - "445 74 5042.0 \n", - "446 162 17364.0 \n", - "447 405 9388.0 \n", - "\n", - " attributes.pangenome-core-size attributes.pangenome-accessory-size \\\n", - "0 803.0 1914.0 \n", - "1 1064.0 2592.0 \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - ".. ... ... \n", - "443 872.0 6252.0 \n", - "444 1696.0 1423.0 \n", - "445 500.0 4542.0 \n", - "446 525.0 16839.0 \n", - "447 567.0 8821.0 \n", - "\n", - " attributes.last-update attributes.first-created \\\n", - "0 2023-04-26T10:45:44.159387 2023-04-26T10:45:44.156146 \n", - "1 2023-04-26T10:12:21.222194 2023-04-26T10:12:21.219735 \n", - "2 2023-04-26T10:20:51.041629 2023-04-26T10:20:51.039628 \n", - "3 2023-04-26T10:45:46.255706 2023-04-26T10:45:46.252869 \n", - "4 2023-04-26T10:05:08.422546 2023-04-26T10:05:08.418890 \n", - ".. ... ... \n", - "443 2021-12-07T19:07:36.435337 2021-12-07T19:07:36.432779 \n", - "444 2021-12-07T18:38:44.085358 2021-12-07T18:38:44.061293 \n", - "445 2021-12-07T18:12:03.286975 2021-12-07T18:12:03.284256 \n", - "446 2021-12-07T19:49:53.957129 2021-12-07T19:49:53.954642 \n", - "447 2021-12-07T20:23:55.443462 2021-12-07T20:23:55.440610 \n", - "\n", - " relationships.catalogue.data.id relationships.catalogue.data.type \\\n", - "0 chicken-gut-v1-0 genome-catalogues \n", - "1 chicken-gut-v1-0 genome-catalogues \n", - "2 chicken-gut-v1-0 genome-catalogues \n", - "3 chicken-gut-v1-0 genome-catalogues \n", - "4 chicken-gut-v1-0 genome-catalogues \n", - ".. ... ... \n", - "443 human-gut-v2-0-1 genome-catalogues \n", - "444 human-gut-v2-0-1 genome-catalogues \n", - "445 human-gut-v2-0-1 genome-catalogues \n", - "446 human-gut-v2-0-1 genome-catalogues \n", - "447 human-gut-v2-0-1 genome-catalogues \n", - "\n", - " relationships.biome.data.id \\\n", - "0 root:Host-associated:Birds:Digestive system:Digestive tube \n", - "1 root:Host-associated:Birds:Digestive system:Digestive tube \n", - "2 root:Host-associated:Birds:Digestive system:Digestive tube \n", - "3 root:Host-associated:Birds:Digestive system:Digestive tube \n", - "4 root:Host-associated:Birds:Digestive system:Digestive tube \n", - ".. ... \n", - "443 root:Host-associated:Human:Digestive system:Large intestine \n", - "444 root:Host-associated:Human:Digestive system:Large intestine \n", - "445 root:Host-associated:Human:Digestive system:Large intestine \n", - "446 root:Host-associated:Human:Digestive system:Large intestine \n", - "447 root:Host-associated:Human:Digestive system:Large intestine \n", - "\n", - " relationships.biome.data.type attributes.ncbi-genome-accession \\\n", - "0 biomes NaN \n", - "1 biomes NaN \n", - "2 biomes NaN \n", - "3 biomes GCA_019113095 \n", - "4 biomes GCA_019116685 \n", - ".. ... ... \n", - "443 biomes NaN \n", - "444 biomes NaN \n", - "445 biomes NaN \n", - "446 biomes NaN \n", - "447 biomes NaN \n", - "\n", - " attributes.ncbi-sample-accession attributes.ncbi-study-accession \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - ".. ... ... \n", - "443 NaN NaN \n", - "444 NaN NaN \n", - "445 NaN NaN \n", - "446 SAMN09736551 PRJNA482748 \n", - "447 SAMN09734707 PRJNA482748 \n", - "\n", - "[448 rows x 37 columns]" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Display the table containing the results of the query\n", "resources_df" @@ -2550,23 +2030,12 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": null, "id": "be264754-f3f9-4f34-bd53-7885c88a1d04", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "resources_df.groupby('relationships.catalogue.data.id').size().plot(kind='pie', autopct='%1.0f%%')\n", "plt.title('Catalogues where the genus Prevotella is found');" @@ -2590,23 +2059,12 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "id": "03fd27c3-c3bb-4d3a-aee1-6cd2d256bef0", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "306" - ] - }, - "execution_count": 206, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resources_df['attributes.taxon-lineage'].nunique()" ] @@ -2621,33 +2079,12 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": null, "id": "fa81e906-e898-424d-ad8a-6a7b639c93b0", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__ 87\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002300055 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002251295 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900316565 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900313215 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella hominis 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002299275 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp900548195 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002251365 3\n", - "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp002480935 3\n", - "Name: attributes.taxon-lineage, dtype: int64" - ] - }, - "execution_count": 213, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resources_df['attributes.taxon-lineage'].value_counts().head(10)" ] @@ -2675,23 +2112,12 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": null, "id": "2664b705-3904-4c40-9ca0-701d12892398", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Save the rows containing novel Prevotella species into a new dataframe\n", "novel_prevotella_df = resources_df[resources_df['attributes.taxon-lineage'] == 'd__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__']\n", @@ -2729,7 +2155,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": null, "id": "d576840c-5535-4958-8031-5952c66532b8", "metadata": { "jupyter": { @@ -2737,2208 +2163,127 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNaNNaNNaNNaNNaN
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415NaN20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0NaN
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325NaN22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0NaN
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035NaN25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0NaN
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965NaN25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0NaN
..................................................................................................................
12525genomesMGYG0000000058452Europe[North America, Europe]MGYG000000005ERS417217NaNNaN39304283235003228.59Isolate99.300.0089.0899.6799.8320.067375594.3088.95d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A22021-12-07T19:58:48.5266952021-12-07T19:58:48.523776root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243622.03040.0582.0NaN
12526genomesMGYG0000000048822Europe[North America, Europe, Asia, NA]MGYG000000004ERS370072NaNNaN36988961059029754.19Isolate98.660.2293.2899.6799.8320.036363495.7186.35d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis252021-12-07T20:09:19.7438372021-12-07T20:09:19.741171root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056245274.0920.04354.0NaN
12527genomesMGYG0000000037373Europe[North America, Europe, Asia, Oceania, South America, NA]MGYG000000003ERS370070NaNNaN32295183515857058.52Isolate100.000.0094.9699.6799.6620.027269093.3889.14d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii11812021-12-07T19:28:56.8140092021-12-07T19:28:56.809967root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562414402.01163.013239.0NaN
12528genomesMGYG0000000029258Europe[North America, Europe, Asia, Oceania, South America]MGYG000000002ERS370064NaNNaN443310910110926742.60Isolate99.370.0082.3595.3786.1919.057392095.5688.85d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis3592021-12-07T20:21:43.8294182021-12-07T20:21:43.826838root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562428872.0661.028211.0NaN
12529genomesMGYG0000000014888Europe[North America, Europe]MGYG000000001ERS370061NaNNaN32196171374725828.26Isolate98.590.7088.2499.7499.8320.063318293.7886.42d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp90236236542021-12-07T18:06:34.7657822021-12-07T18:06:34.762463root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243154.01350.01804.0NaN
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"12527 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii \n", - "12528 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis \n", - "12529 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp902362365 \n", - "\n", - " attributes.num-genomes-total attributes.last-update \\\n", - "0 1 2023-07-07T11:35:45.488929 \n", - "1 2 2023-07-07T11:37:17.515368 \n", - "2 7 2023-07-07T11:37:26.626388 \n", - "3 12 2023-07-07T11:35:14.099161 \n", - "4 19 2023-07-07T11:33:02.552198 \n", - "... ... ... \n", - "12525 2 2021-12-07T19:58:48.526695 \n", - "12526 25 2021-12-07T20:09:19.743837 \n", - "12527 1181 2021-12-07T19:28:56.814009 \n", - "12528 359 2021-12-07T20:21:43.829418 \n", - "12529 4 2021-12-07T18:06:34.765782 \n", - "\n", - " attributes.first-created \\\n", - "0 2023-07-07T11:35:45.486483 \n", - "1 2023-07-07T11:37:17.512635 \n", - "2 2023-07-07T11:37:26.624241 \n", - "3 2023-07-07T11:35:14.096893 \n", - "4 2023-07-07T11:33:02.549672 \n", - "... ... \n", - "12525 2021-12-07T19:58:48.523776 \n", - "12526 2021-12-07T20:09:19.741171 \n", - "12527 2021-12-07T19:28:56.809967 \n", - "12528 2021-12-07T20:21:43.826838 \n", - "12529 2021-12-07T18:06:34.762463 \n", - "\n", - " relationships.biome.data.id \\\n", - "0 root:Host-associated:Insecta:Digestive system \n", - "1 root:Host-associated:Insecta:Digestive system \n", - "2 root:Host-associated:Insecta:Digestive system \n", - "3 root:Host-associated:Insecta:Digestive system \n", - "4 root:Host-associated:Insecta:Digestive system \n", - "... ... \n", - "12525 root:Host-associated:Human:Digestive system:Large intestine \n", - "12526 root:Host-associated:Human:Digestive system:Large intestine \n", - "12527 root:Host-associated:Human:Digestive system:Large intestine \n", - "12528 root:Host-associated:Human:Digestive system:Large intestine \n", - "12529 root:Host-associated:Human:Digestive system:Large intestine \n", - "\n", - " relationships.biome.data.type relationships.catalogue.data.id \\\n", - "0 biomes honeybee-gut-v1-0 \n", - "1 biomes honeybee-gut-v1-0 \n", - "2 biomes honeybee-gut-v1-0 \n", - "3 biomes honeybee-gut-v1-0 \n", - "4 biomes honeybee-gut-v1-0 \n", - "... ... ... \n", - "12525 biomes human-gut-v2-0-1 \n", - "12526 biomes human-gut-v2-0-1 \n", - "12527 biomes human-gut-v2-0-1 \n", - "12528 biomes human-gut-v2-0-1 \n", - "12529 biomes human-gut-v2-0-1 \n", - "\n", - " relationships.catalogue.data.type attributes.ena-study-accession \\\n", - "0 genome-catalogues NaN \n", - "1 genome-catalogues SRP308969 \n", - "2 genome-catalogues SRP308969 \n", - "3 genome-catalogues SRP308969 \n", - "4 genome-catalogues SRP308969 \n", - "... ... ... \n", - "12525 genome-catalogues ERP105624 \n", - "12526 genome-catalogues ERP105624 \n", - "12527 genome-catalogues ERP105624 \n", - "12528 genome-catalogues ERP105624 \n", - "12529 genome-catalogues ERP105624 \n", - "\n", - " attributes.pangenome-size attributes.pangenome-core-size \\\n", - "0 NaN NaN \n", - "1 1735.0 1688.0 \n", - "2 2230.0 1526.0 \n", - "3 2444.0 1872.0 \n", - "4 3216.0 1678.0 \n", - "... ... ... \n", - "12525 3622.0 3040.0 \n", - "12526 5274.0 920.0 \n", - "12527 14402.0 1163.0 \n", - "12528 28872.0 661.0 \n", - "12529 3154.0 1350.0 \n", - "\n", - " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \n", - "0 NaN NaN \n", - "1 47.0 NaN \n", - "2 704.0 NaN \n", - "3 572.0 NaN \n", - "4 1538.0 NaN \n", - "... ... ... \n", - "12525 582.0 NaN \n", - "12526 4354.0 NaN \n", - "12527 13239.0 NaN \n", - "12528 28211.0 NaN \n", - "12529 1804.0 NaN \n", - "\n", - "[12530 rows x 37 columns]" - ] - }, - "execution_count": 281, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "endpoint = \"genomes\"\n", - "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", - " resources_all = map(lambda r: r.json, mgnify.iterate(endpoint))\n", - "resources_all_df = pd.json_normalize(resources_all)\n", - "resources_all_df.to_parquet('all_genome_resources_Aug2023.parquet')" - ] - }, - { - "cell_type": "markdown", - "id": "75956486-d5a3-46dd-aaa0-0fa37fb9b94e", - "metadata": {}, - "source": [ - "The table with all genomes has been saved as a Parquet file. This file format allows for efficient data storage and retrieval.\n", - "The file is located in ../example-data/genomes/ directory and is called ```all_genome_resources_Aug2023.parquet```.\n", - "\n", - "Let's load it into a dataframe ```all_genomes_df```:" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "id": "b3aa6767-a07a-4c61-88aa-011183b6fb68", - "metadata": { - "tags": [] - }, "outputs": [], "source": [ - "all_genomes_df = pd.read_parquet('../example-data/genomes/all_genome_resources_Aug2023.parquet')" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "id": "96ecd56e-6208-4b2c-bba9-72ea839b12ce", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ncbi-genome-accessionattributes.ncbi-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.last-updateattributes.first-createdrelationships.biome.data.idrelationships.biome.data.typerelationships.catalogue.data.idrelationships.catalogue.data.typeattributes.ena-study-accessionattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.ncbi-sample-accession
0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNoneNaNNaNNaNNone
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415None20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0None
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325None22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0None
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035None25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0None
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965None25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0None
\n", - "
" - ], - "text/plain": [ - " type id attributes.genome-id attributes.geographic-origin \\\n", - "0 genomes MGYG000321626 17285 Europe \n", - "1 genomes MGYG000321625 17333 Asia \n", - "2 genomes MGYG000321620 17338 Asia \n", - "3 genomes MGYG000321606 17269 Asia \n", - "4 genomes MGYG000321602 17200 Asia \n", - "\n", - " attributes.geographic-range attributes.accession \\\n", - "0 [] MGYG000321626 \n", - "1 [Asia] MGYG000321625 \n", - "2 [Asia] MGYG000321620 \n", - "3 [Asia] MGYG000321606 \n", - "4 [Asia] MGYG000321602 \n", - "\n", - " attributes.ena-sample-accession attributes.ncbi-genome-accession \\\n", - "0 SAMN33097420 GCA_028655675 \n", - "1 SAMN18106362 GCA_026537415 \n", - "2 SAMN18106366 GCA_026537325 \n", - "3 SAMN18106382 GCA_026537035 \n", - "4 SAMN18106384 GCA_026536965 \n", - "\n", - " attributes.ncbi-study-accession attributes.length attributes.num-contigs \\\n", - "0 PRJNA931735 1881475 145 \n", - "1 None 2042703 39 \n", - "2 None 2224927 19 \n", - "3 None 2522333 48 \n", - "4 None 2528524 34 \n", - "\n", - " attributes.n-50 attributes.gc-content attributes.type \\\n", - "0 32562 49.81 Isolate \n", - "1 281954 55.50 MAG \n", - "2 390684 59.87 MAG \n", - "3 138263 34.23 MAG \n", - "4 177044 35.11 MAG \n", - "\n", - " attributes.completeness attributes.contamination attributes.rna-5s \\\n", - "0 99.03 0.00 93.28 \n", - "1 100.00 0.00 0.00 \n", - "2 97.64 1.78 0.00 \n", - "3 98.87 0.56 94.96 \n", - "4 98.31 0.00 94.96 \n", - "\n", - " attributes.rna-16s attributes.rna-23s attributes.trnas \\\n", - "0 99.41 97.64 20.0 \n", - "1 99.61 99.28 20.0 \n", - "2 99.67 99.18 20.0 \n", - "3 99.67 99.86 19.0 \n", - "4 99.67 99.86 20.0 \n", - "\n", - " attributes.nc-rnas attributes.num-proteins attributes.eggnog-coverage \\\n", - "0 26 1803 95.73 \n", - "1 21 1842 89.36 \n", - "2 14 1811 95.42 \n", - "3 26 2241 95.85 \n", - "4 32 2249 94.49 \n", - "\n", - " attributes.ipr-coverage \\\n", - "0 89.24 \n", - "1 89.03 \n", - "2 93.04 \n", - "3 92.64 \n", - "4 92.26 \n", - "\n", - " attributes.taxon-lineage \\\n", - "0 d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii \n", - "1 d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__ \n", - "2 d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__ \n", - "3 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075 \n", - "4 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E \n", - "\n", - " attributes.num-genomes-total attributes.last-update \\\n", - "0 1 2023-07-07T11:35:45.488929 \n", - "1 2 2023-07-07T11:37:17.515368 \n", - "2 7 2023-07-07T11:37:26.626388 \n", - "3 12 2023-07-07T11:35:14.099161 \n", - "4 19 2023-07-07T11:33:02.552198 \n", - "\n", - " attributes.first-created relationships.biome.data.id \\\n", - "0 2023-07-07T11:35:45.486483 root:Host-associated:Insecta:Digestive system \n", - "1 2023-07-07T11:37:17.512635 root:Host-associated:Insecta:Digestive system \n", - "2 2023-07-07T11:37:26.624241 root:Host-associated:Insecta:Digestive system \n", - "3 2023-07-07T11:35:14.096893 root:Host-associated:Insecta:Digestive system \n", - "4 2023-07-07T11:33:02.549672 root:Host-associated:Insecta:Digestive system \n", - "\n", - " relationships.biome.data.type relationships.catalogue.data.id \\\n", - "0 biomes honeybee-gut-v1-0 \n", - "1 biomes honeybee-gut-v1-0 \n", - "2 biomes honeybee-gut-v1-0 \n", - "3 biomes honeybee-gut-v1-0 \n", - "4 biomes honeybee-gut-v1-0 \n", - "\n", - " relationships.catalogue.data.type attributes.ena-study-accession \\\n", - "0 genome-catalogues None \n", - "1 genome-catalogues SRP308969 \n", - "2 genome-catalogues SRP308969 \n", - "3 genome-catalogues SRP308969 \n", - "4 genome-catalogues SRP308969 \n", - "\n", - " attributes.pangenome-size attributes.pangenome-core-size \\\n", - "0 NaN NaN \n", - "1 1735.0 1688.0 \n", - "2 2230.0 1526.0 \n", - "3 2444.0 1872.0 \n", - "4 3216.0 1678.0 \n", - "\n", - " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \n", - "0 NaN None \n", - "1 47.0 None \n", - "2 704.0 None \n", - "3 572.0 None \n", - "4 1538.0 None " - ] - }, - "execution_count": 294, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's check what this dataframe looks like\n", - "all_genomes_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 359, - "id": "6e0baef0-f81e-44f3-bac4-69a11ca27c96", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 12530.000000\n", - "mean 47.652266\n", - "std 10.627749\n", - "min 22.940000\n", - "25% 40.070000\n", - "50% 48.240000\n", - "75% 56.230000\n", - "max 74.840000\n", - "Name: attributes.gc-content, dtype: float64" - ] - }, - "execution_count": 359, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Using Pandas we could also do some quick analysis, for example, what is the average GC content of the \n", - "# genomes in MGnify? We can use the .describe() method that calculates statistical information.\n", - "all_genomes_df['attributes.gc-content'].describe()\n" - ] - }, - { - "cell_type": "markdown", - "id": "b21346da-7af6-4c1c-86fc-20a62da31f7c", - "metadata": {}, - "source": [ - "This is telling us that in total there are 12,530 GC content records (matching the total number of species representative genomes) and the mean GC% is 47.65%.\n", - "\n", - "Now let's plot the phyla. We need to do some prep work first:" - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "id": "a5fc7b37-3f13-4303-866a-bd9b572edc2e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Split taxon lineage into columns (separate column for each taxonomic level)\n", - "\n", - "# Write each possible taxonomic level into a list called \"features\"\n", - "features = ['domain', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n", - "\n", - "\n", - "def get_lineage_column(lineage_str, i):\n", - " # This is a Python function that takes in a full taxonomic lineage, for example,\n", - " # d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii\n", - " # and returns the value in the requested position. In Python we count from 0 so \n", - " # if we requested the taxon name in position 1 (i = 1), the function would \n", - " # return \"p__Bacillota\"\n", - "\n", - " lineage_split = lineage_str.split(';')\n", - " return lineage_split[i] \n", - "\n", - "\n", - "# Apply the function to create the new columns in the pandas DataFrame by splitting column attributes.taxon-lineage\n", - "for i, feature in enumerate(features):\n", - " all_genomes_df[feature] = all_genomes_df['attributes.taxon-lineage'].apply(lambda x: get_lineage_column(x, i))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "id": "93a7404a-e8e2-4427-b1da-497b94839e5d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.genome-idattributes.geographic-originattributes.geographic-rangeattributes.accessionattributes.ena-sample-accessionattributes.ncbi-genome-accessionattributes.ncbi-study-accessionattributes.lengthattributes.num-contigsattributes.n-50attributes.gc-contentattributes.typeattributes.completenessattributes.contaminationattributes.rna-5sattributes.rna-16sattributes.rna-23sattributes.trnasattributes.nc-rnasattributes.num-proteinsattributes.eggnog-coverageattributes.ipr-coverageattributes.taxon-lineageattributes.num-genomes-totalattributes.last-updateattributes.first-createdrelationships.biome.data.idrelationships.biome.data.typerelationships.catalogue.data.idrelationships.catalogue.data.typeattributes.ena-study-accessionattributes.pangenome-sizeattributes.pangenome-core-sizeattributes.pangenome-accessory-sizeattributes.ncbi-sample-accessiondomainphylumclassorderfamilygenusspecies
0genomesMGYG00032162617285Europe[]MGYG000321626SAMN33097420GCA_028655675PRJNA93173518814751453256249.81Isolate99.030.0093.2899.4197.6420.026180395.7389.24d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii12023-07-07T11:35:45.4889292023-07-07T11:35:45.486483root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesNoneNaNNaNNaNNoned__Bacteriap__Bacillotac__Bacillio__Lactobacillalesf__Lactobacillaceaeg__Lactobacilluss__Lactobacillus delbrueckii
1genomesMGYG00032162517333Asia[Asia]MGYG000321625SAMN18106362GCA_026537415None20427033928195455.50MAG100.000.000.0099.6199.2820.021184289.3689.03d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__22023-07-07T11:37:17.5153682023-07-07T11:37:17.512635root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089691735.01688.047.0Noned__Bacteriap__Pseudomonadotac__Alphaproteobacteriao__Acetobacteralesf__Acetobacteraceaeg__Bombellas__
2genomesMGYG00032162017338Asia[Asia]MGYG000321620SAMN18106366GCA_026537325None22249271939068459.87MAG97.641.780.0099.6799.1820.014181195.4293.04d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__72023-07-07T11:37:26.6263882023-07-07T11:37:26.624241root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692230.01526.0704.0Noned__Bacteriap__Actinomycetotac__Actinomycetiao__Actinomycetalesf__Bifidobacteriaceaeg__Bifidobacteriums__
3genomesMGYG00032160617269Asia[Asia]MGYG000321606SAMN18106382GCA_026537035None25223334813826334.23MAG98.870.5694.9699.6799.8619.026224195.8592.64d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075122023-07-07T11:35:14.0991612023-07-07T11:35:14.096893root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089692444.01872.0572.0Noned__Bacteriap__Pseudomonadotac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceaeg__Gilliamellas__Gilliamella sp945273075
4genomesMGYG00032160217200Asia[Asia]MGYG000321602SAMN18106384GCA_026536965None25285243417704435.11MAG98.310.0094.9699.6799.8620.032224994.4992.26d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E192023-07-07T11:33:02.5521982023-07-07T11:33:02.549672root:Host-associated:Insecta:Digestive systembiomeshoneybee-gut-v1-0genome-cataloguesSRP3089693216.01678.01538.0Noned__Bacteriap__Pseudomonadotac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceaeg__Gilliamellas__Gilliamella apicola_E
.......................................................................................................................................
12525genomesMGYG0000000058452Europe[North America, Europe]MGYG000000005ERS417217NoneNone39304283235003228.59Isolate99.300.0089.0899.6799.8320.067375594.3088.95d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A22021-12-07T19:58:48.5266952021-12-07T19:58:48.523776root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243622.03040.0582.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Peptostreptococcalesf__Peptostreptococcaceaeg__Terrisporobacters__Terrisporobacter glycolicus_A
12526genomesMGYG0000000048822Europe[North America, Europe, Asia, NA]MGYG000000004ERS370072NoneNone36988961059029754.19Isolate98.660.2293.2899.6799.8320.036363495.7186.35d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis252021-12-07T20:09:19.7438372021-12-07T20:09:19.741171root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056245274.0920.04354.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Oscillospiralesf__Ruminococcaceaeg__Anaerotruncuss__Anaerotruncus colihominis
12527genomesMGYG0000000037373Europe[North America, Europe, Asia, Oceania, South America, NA]MGYG000000003ERS370070NoneNone32295183515857058.52Isolate100.000.0094.9699.6799.6620.027269093.3889.14d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii11812021-12-07T19:28:56.8140092021-12-07T19:28:56.809967root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562414402.01163.013239.0Noned__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Rikenellaceaeg__Alistipess__Alistipes shahii
12528genomesMGYG0000000029258Europe[North America, Europe, Asia, Oceania, South America]MGYG000000002ERS370064NoneNone443310910110926742.60Isolate99.370.0082.3595.3786.1919.057392095.5688.85d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis3592021-12-07T20:21:43.8294182021-12-07T20:21:43.826838root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP10562428872.0661.028211.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Lachnospiralesf__Lachnospiraceaeg__Blautia_As__Blautia_A faecis
12529genomesMGYG0000000014888Europe[North America, Europe]MGYG000000001ERS370061NoneNone32196171374725828.26Isolate98.590.7088.2499.7499.8320.063318293.7886.42d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp90236236542021-12-07T18:06:34.7657822021-12-07T18:06:34.762463root:Host-associated:Human:Digestive system:Large intestinebiomeshuman-gut-v2-0-1genome-cataloguesERP1056243154.01350.01804.0Noned__Bacteriap__Firmicutes_Ac__Clostridiao__Peptostreptococcalesf__Peptostreptococcaceaeg__GCA-900066495s__GCA-900066495 sp902362365
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12530 rows × 44 columns

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" - ], - "text/plain": [ - " type id attributes.genome-id \\\n", - "0 genomes MGYG000321626 17285 \n", - "1 genomes MGYG000321625 17333 \n", - "2 genomes MGYG000321620 17338 \n", - "3 genomes MGYG000321606 17269 \n", - "4 genomes MGYG000321602 17200 \n", - "... ... ... ... \n", - "12525 genomes MGYG000000005 8452 \n", - "12526 genomes MGYG000000004 8822 \n", - "12527 genomes MGYG000000003 7373 \n", - "12528 genomes MGYG000000002 9258 \n", - "12529 genomes MGYG000000001 4888 \n", - "\n", - " attributes.geographic-origin \\\n", - "0 Europe \n", - "1 Asia \n", - "2 Asia \n", - "3 Asia \n", - "4 Asia \n", - "... ... \n", - "12525 Europe \n", - "12526 Europe \n", - "12527 Europe \n", - "12528 Europe \n", - "12529 Europe \n", - "\n", - " attributes.geographic-range \\\n", - "0 [] \n", - "1 [Asia] \n", - "2 [Asia] \n", - "3 [Asia] \n", - "4 [Asia] \n", - "... ... \n", - "12525 [North America, Europe] \n", - "12526 [North America, Europe, Asia, NA] \n", - "12527 [North America, Europe, Asia, Oceania, South America, NA] \n", - "12528 [North America, Europe, Asia, Oceania, South America] \n", - "12529 [North America, Europe] \n", - "\n", - " attributes.accession attributes.ena-sample-accession \\\n", - "0 MGYG000321626 SAMN33097420 \n", - "1 MGYG000321625 SAMN18106362 \n", - "2 MGYG000321620 SAMN18106366 \n", - "3 MGYG000321606 SAMN18106382 \n", - "4 MGYG000321602 SAMN18106384 \n", - "... ... ... \n", - "12525 MGYG000000005 ERS417217 \n", - "12526 MGYG000000004 ERS370072 \n", - "12527 MGYG000000003 ERS370070 \n", - "12528 MGYG000000002 ERS370064 \n", - "12529 MGYG000000001 ERS370061 \n", - "\n", - " attributes.ncbi-genome-accession attributes.ncbi-study-accession \\\n", - "0 GCA_028655675 PRJNA931735 \n", - "1 GCA_026537415 None \n", - "2 GCA_026537325 None \n", - "3 GCA_026537035 None \n", - "4 GCA_026536965 None \n", - "... ... ... \n", - "12525 None None \n", - "12526 None None \n", - "12527 None None \n", - "12528 None None \n", - "12529 None None \n", - "\n", - " attributes.length attributes.num-contigs attributes.n-50 \\\n", - "0 1881475 145 32562 \n", - "1 2042703 39 281954 \n", - "2 2224927 19 390684 \n", - "3 2522333 48 138263 \n", - "4 2528524 34 177044 \n", - "... ... ... ... \n", - "12525 3930428 32 350032 \n", - "12526 3698896 105 90297 \n", - "12527 3229518 35 158570 \n", - "12528 4433109 101 109267 \n", - "12529 3219617 137 47258 \n", - "\n", - " attributes.gc-content attributes.type attributes.completeness \\\n", - "0 49.81 Isolate 99.03 \n", - "1 55.50 MAG 100.00 \n", - "2 59.87 MAG 97.64 \n", - "3 34.23 MAG 98.87 \n", - "4 35.11 MAG 98.31 \n", - "... ... ... ... \n", - "12525 28.59 Isolate 99.30 \n", - "12526 54.19 Isolate 98.66 \n", - "12527 58.52 Isolate 100.00 \n", - "12528 42.60 Isolate 99.37 \n", - "12529 28.26 Isolate 98.59 \n", - "\n", - " attributes.contamination attributes.rna-5s attributes.rna-16s \\\n", - "0 0.00 93.28 99.41 \n", - "1 0.00 0.00 99.61 \n", - "2 1.78 0.00 99.67 \n", - "3 0.56 94.96 99.67 \n", - "4 0.00 94.96 99.67 \n", - "... ... ... ... \n", - "12525 0.00 89.08 99.67 \n", - "12526 0.22 93.28 99.67 \n", - "12527 0.00 94.96 99.67 \n", - "12528 0.00 82.35 95.37 \n", - "12529 0.70 88.24 99.74 \n", - "\n", - " attributes.rna-23s attributes.trnas attributes.nc-rnas \\\n", - "0 97.64 20.0 26 \n", - "1 99.28 20.0 21 \n", - "2 99.18 20.0 14 \n", - "3 99.86 19.0 26 \n", - "4 99.86 20.0 32 \n", - "... ... ... ... \n", - "12525 99.83 20.0 67 \n", - "12526 99.83 20.0 36 \n", - "12527 99.66 20.0 27 \n", - "12528 86.19 19.0 57 \n", - "12529 99.83 20.0 63 \n", - "\n", - " attributes.num-proteins attributes.eggnog-coverage \\\n", - "0 1803 95.73 \n", - "1 1842 89.36 \n", - "2 1811 95.42 \n", - "3 2241 95.85 \n", - "4 2249 94.49 \n", - "... ... ... \n", - "12525 3755 94.30 \n", - "12526 3634 95.71 \n", - "12527 2690 93.38 \n", - "12528 3920 95.56 \n", - "12529 3182 93.78 \n", - "\n", - " attributes.ipr-coverage \\\n", - "0 89.24 \n", - "1 89.03 \n", - "2 93.04 \n", - "3 92.64 \n", - "4 92.26 \n", - "... ... \n", - "12525 88.95 \n", - "12526 86.35 \n", - "12527 89.14 \n", - "12528 88.85 \n", - "12529 86.42 \n", - "\n", - " attributes.taxon-lineage \\\n", - "0 d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii \n", - "1 d__Bacteria;p__Pseudomonadota;c__Alphaproteobacteria;o__Acetobacterales;f__Acetobacteraceae;g__Bombella;s__ \n", - "2 d__Bacteria;p__Actinomycetota;c__Actinomycetia;o__Actinomycetales;f__Bifidobacteriaceae;g__Bifidobacterium;s__ \n", - "3 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella sp945273075 \n", - "4 d__Bacteria;p__Pseudomonadota;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Gilliamella;s__Gilliamella apicola_E \n", - "... ... \n", - "12525 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__Terrisporobacter;s__Terrisporobacter glycolicus_A \n", - "12526 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Anaerotruncus;s__Anaerotruncus colihominis \n", - "12527 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__Alistipes shahii \n", - "12528 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Blautia_A;s__Blautia_A faecis \n", - "12529 d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Peptostreptococcales;f__Peptostreptococcaceae;g__GCA-900066495;s__GCA-900066495 sp902362365 \n", - "\n", - " attributes.num-genomes-total attributes.last-update \\\n", - "0 1 2023-07-07T11:35:45.488929 \n", - "1 2 2023-07-07T11:37:17.515368 \n", - "2 7 2023-07-07T11:37:26.626388 \n", - "3 12 2023-07-07T11:35:14.099161 \n", - "4 19 2023-07-07T11:33:02.552198 \n", - "... ... ... \n", - "12525 2 2021-12-07T19:58:48.526695 \n", - "12526 25 2021-12-07T20:09:19.743837 \n", - "12527 1181 2021-12-07T19:28:56.814009 \n", - "12528 359 2021-12-07T20:21:43.829418 \n", - "12529 4 2021-12-07T18:06:34.765782 \n", - "\n", - " attributes.first-created \\\n", - "0 2023-07-07T11:35:45.486483 \n", - "1 2023-07-07T11:37:17.512635 \n", - "2 2023-07-07T11:37:26.624241 \n", - "3 2023-07-07T11:35:14.096893 \n", - "4 2023-07-07T11:33:02.549672 \n", - "... ... \n", - "12525 2021-12-07T19:58:48.523776 \n", - "12526 2021-12-07T20:09:19.741171 \n", - "12527 2021-12-07T19:28:56.809967 \n", - "12528 2021-12-07T20:21:43.826838 \n", - "12529 2021-12-07T18:06:34.762463 \n", - "\n", - " relationships.biome.data.id \\\n", - "0 root:Host-associated:Insecta:Digestive system \n", - "1 root:Host-associated:Insecta:Digestive system \n", - "2 root:Host-associated:Insecta:Digestive system \n", - "3 root:Host-associated:Insecta:Digestive system \n", - "4 root:Host-associated:Insecta:Digestive system \n", - "... ... \n", - "12525 root:Host-associated:Human:Digestive system:Large intestine \n", - "12526 root:Host-associated:Human:Digestive system:Large intestine \n", - "12527 root:Host-associated:Human:Digestive system:Large intestine \n", - "12528 root:Host-associated:Human:Digestive system:Large intestine \n", - "12529 root:Host-associated:Human:Digestive system:Large intestine \n", - "\n", - " relationships.biome.data.type relationships.catalogue.data.id \\\n", - "0 biomes honeybee-gut-v1-0 \n", - "1 biomes honeybee-gut-v1-0 \n", - "2 biomes honeybee-gut-v1-0 \n", - "3 biomes honeybee-gut-v1-0 \n", - "4 biomes honeybee-gut-v1-0 \n", - "... ... ... \n", - "12525 biomes human-gut-v2-0-1 \n", - "12526 biomes human-gut-v2-0-1 \n", - "12527 biomes human-gut-v2-0-1 \n", - "12528 biomes human-gut-v2-0-1 \n", - "12529 biomes human-gut-v2-0-1 \n", - "\n", - " relationships.catalogue.data.type attributes.ena-study-accession \\\n", - "0 genome-catalogues None \n", - "1 genome-catalogues SRP308969 \n", - "2 genome-catalogues SRP308969 \n", - "3 genome-catalogues SRP308969 \n", - "4 genome-catalogues SRP308969 \n", - "... ... ... \n", - "12525 genome-catalogues ERP105624 \n", - "12526 genome-catalogues ERP105624 \n", - "12527 genome-catalogues ERP105624 \n", - "12528 genome-catalogues ERP105624 \n", - "12529 genome-catalogues ERP105624 \n", - "\n", - " attributes.pangenome-size attributes.pangenome-core-size \\\n", - "0 NaN NaN \n", - "1 1735.0 1688.0 \n", - "2 2230.0 1526.0 \n", - "3 2444.0 1872.0 \n", - "4 3216.0 1678.0 \n", - "... ... ... \n", - "12525 3622.0 3040.0 \n", - "12526 5274.0 920.0 \n", - "12527 14402.0 1163.0 \n", - "12528 28872.0 661.0 \n", - "12529 3154.0 1350.0 \n", - "\n", - " attributes.pangenome-accessory-size attributes.ncbi-sample-accession \\\n", - "0 NaN None \n", - "1 47.0 None \n", - "2 704.0 None \n", - "3 572.0 None \n", - "4 1538.0 None \n", - "... ... ... \n", - "12525 582.0 None \n", - "12526 4354.0 None \n", - "12527 13239.0 None \n", - "12528 28211.0 None \n", - "12529 1804.0 None \n", - "\n", - " domain phylum class \\\n", - "0 d__Bacteria p__Bacillota c__Bacilli \n", - "1 d__Bacteria p__Pseudomonadota c__Alphaproteobacteria \n", - "2 d__Bacteria p__Actinomycetota c__Actinomycetia \n", - "3 d__Bacteria p__Pseudomonadota c__Gammaproteobacteria \n", - "4 d__Bacteria p__Pseudomonadota c__Gammaproteobacteria \n", - "... ... ... ... \n", - "12525 d__Bacteria p__Firmicutes_A c__Clostridia \n", - "12526 d__Bacteria p__Firmicutes_A c__Clostridia \n", - "12527 d__Bacteria p__Bacteroidota c__Bacteroidia \n", - "12528 d__Bacteria p__Firmicutes_A c__Clostridia \n", - "12529 d__Bacteria p__Firmicutes_A c__Clostridia \n", - "\n", - " order family genus \\\n", - "0 o__Lactobacillales f__Lactobacillaceae g__Lactobacillus \n", - "1 o__Acetobacterales f__Acetobacteraceae g__Bombella \n", - "2 o__Actinomycetales f__Bifidobacteriaceae g__Bifidobacterium \n", - "3 o__Enterobacterales f__Enterobacteriaceae g__Gilliamella \n", - "4 o__Enterobacterales f__Enterobacteriaceae g__Gilliamella \n", - "... ... ... ... \n", - "12525 o__Peptostreptococcales f__Peptostreptococcaceae g__Terrisporobacter \n", - "12526 o__Oscillospirales f__Ruminococcaceae g__Anaerotruncus \n", - "12527 o__Bacteroidales f__Rikenellaceae g__Alistipes \n", - "12528 o__Lachnospirales f__Lachnospiraceae g__Blautia_A \n", - "12529 o__Peptostreptococcales f__Peptostreptococcaceae g__GCA-900066495 \n", - "\n", - " species \n", - "0 s__Lactobacillus delbrueckii \n", - "1 s__ \n", - "2 s__ \n", - "3 s__Gilliamella sp945273075 \n", - "4 s__Gilliamella apicola_E \n", - "... ... \n", - "12525 s__Terrisporobacter glycolicus_A \n", - "12526 s__Anaerotruncus colihominis \n", - "12527 s__Alistipes shahii \n", - "12528 s__Blautia_A faecis \n", - "12529 s__GCA-900066495 sp902362365 \n", - "\n", - "[12530 rows x 44 columns]" - ] - }, - "execution_count": 296, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ + "endpoint = \"genomes\"\n", + "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + " resources_all = map(lambda r: r.json, mgnify.iterate(endpoint))\n", + "resources_all_df = pd.json_normalize(resources_all)\n", + "resources_all_df.to_parquet('all_genome_resources_Aug2023.parquet')" + ] + }, + { + "cell_type": "markdown", + "id": "75956486-d5a3-46dd-aaa0-0fa37fb9b94e", + "metadata": {}, + "source": [ + "The table with all genomes has been saved as a Parquet file. This file format allows for efficient data storage and retrieval.\n", + "The file is located in ../example-data/genomes/ directory and is called ```all_genome_resources_Aug2023.parquet```.\n", + "\n", + "Let's load it into a dataframe ```all_genomes_df```:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3aa6767-a07a-4c61-88aa-011183b6fb68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "all_genomes_df = pd.read_parquet('../example-data/genomes/all_genome_resources_Aug2023.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96ecd56e-6208-4b2c-bba9-72ea839b12ce", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Let's check what this dataframe looks like\n", + "all_genomes_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e0baef0-f81e-44f3-bac4-69a11ca27c96", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Using Pandas we could also do some quick analysis, for example, what is the average GC content of the \n", + "# genomes in MGnify? We can use the .describe() method that calculates statistical information.\n", + "all_genomes_df['attributes.gc-content'].describe()\n" + ] + }, + { + "cell_type": "markdown", + "id": "b21346da-7af6-4c1c-86fc-20a62da31f7c", + "metadata": {}, + "source": [ + "This is telling us that in total there are 12,530 GC content records (matching the total number of species representative genomes) and the mean GC% is 47.65%.\n", + "\n", + "Now let's plot the phyla. We need to do some prep work first:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5fc7b37-3f13-4303-866a-bd9b572edc2e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Split taxon lineage into columns (separate column for each taxonomic level)\n", + "\n", + "# Write each possible taxonomic level into a list called \"features\"\n", + "features = ['domain', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n", + "\n", + "\n", + "def get_lineage_column(lineage_str, i):\n", + " # This is a Python function that takes in a full taxonomic lineage, for example,\n", + " # d__Bacteria;p__Bacillota;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__Lactobacillus delbrueckii\n", + " # and returns the value in the requested position. In Python we count from 0 so \n", + " # if we requested the taxon name in position 1 (i = 1), the function would \n", + " # return \"p__Bacillota\"\n", + "\n", + " lineage_split = lineage_str.split(';')\n", + " return lineage_split[i] \n", + "\n", + "\n", + "# Apply the function to create the new columns in the pandas DataFrame by splitting column attributes.taxon-lineage\n", + "for i, feature in enumerate(features):\n", + " all_genomes_df[feature] = all_genomes_df['attributes.taxon-lineage'].apply(lambda x: get_lineage_column(x, i))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93a7404a-e8e2-4427-b1da-497b94839e5d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ "# Let's see what the new columns look like (scroll to the right)\n", "all_genomes_df" ] }, { "cell_type": "code", - "execution_count": 348, + "execution_count": null, "id": "1c689272-26b9-41b1-9995-67714f661456", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "p__Firmicutes_A 4489\n", - "p__Bacteroidota 2176\n", - "p__Proteobacteria 1397\n", - "p__Firmicutes 1268\n", - "p__Actinobacteriota 1239\n", - "p__Firmicutes_C 287\n", - "p__Verrucomicrobiota 259\n", - "p__Cyanobacteria 237\n", - "p__Spirochaetota 136\n", - "p__Patescibacteria 119\n", - "p__Pseudomonadota 87\n", - "p__Campylobacterota 85\n", - "p__Chloroflexota 75\n", - "p__Fusobacteriota 73\n", - "p__Planctomycetota 58\n", - "p__Methanobacteriota 55\n", - "p__Desulfobacterota 48\n", - "p__Elusimicrobiota 44\n", - "p__Marinisomatota 43\n", - "p__Bacillota 40\n", - "Name: phylum, dtype: int64" - ] - }, - "execution_count": 348, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Use the value_counts() to get the count of each phylum in the corresponding column.\n", "# Sort values from largest to smallest by using method .sort_values(ascending=False) and keep just the first 20\n", @@ -4958,23 +2303,12 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": null, "id": "0ac21473-cef7-497b-b9e0-2f4872e517cb", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "12215" - ] - }, - "execution_count": 349, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "phylum_counts.sum()" ] @@ -4989,23 +2323,12 @@ }, { "cell_type": "code", - "execution_count": 350, + "execution_count": null, "id": "ada2aec3-a17f-455f-b9af-4abdc9052c33", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", 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attributes.completenessattributes.contamination
count12530.00000012530.000000
mean89.5693881.025984
std10.6348151.096434
min50.0600000.000000
25%85.1200000.000000
50%93.0300000.740000
75%97.5500001.570000
max100.0000005.000000
\n", - "
" - ], - "text/plain": [ - " attributes.completeness attributes.contamination\n", - "count 12530.000000 12530.000000\n", - "mean 89.569388 1.025984\n", - "std 10.634815 1.096434\n", - "min 50.060000 0.000000\n", - "25% 85.120000 0.000000\n", - "50% 93.030000 0.740000\n", - "75% 97.550000 1.570000\n", - "max 100.000000 5.000000" - ] - }, - "execution_count": 351, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "all_genomes_df[['attributes.completeness', 'attributes.contamination']].describe()\n" ] @@ -5145,23 +2381,12 @@ }, { "cell_type": "code", - "execution_count": 353, + "execution_count": null, "id": "dc45345b-db52-4cf2-9e3b-6a920f9a6f26", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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qVKnif0Z/tD3pI9/j/Ntvv6l169aqU6eOXnjhBaWkpCgqKkoff/yxRowYke+tTcHo0qWL7r33Xk2ZMkUPPvig3njjDV188cUF/pIpyLGODhSm3NxcnX/++XrhhRcKHD8ywCVKlChwPTM75TkcbZsFLT/8cnJzc1WxYsWAEB0u75e2x+PR22+/rUWLFumDDz7QJ598oltvvVXPP/+8Fi1aVODe6KnO+WRvh27duun222/X5s2b1a5dO/9RiiPlvTL+p59+UosWLfzLU1JS/D+jvD3Mwpprnh49emjgwIH6888/lZ2drUWLFumll1467vnq1Kmj77//XgcPHgx4d8HRnrTkPT5vuukm9ezZs8B1jjzv6b6uJ3PfPPJycnNzdeWVV+r+++8vcN28J9gVK1bU8uXL9cknnygtLU1paWmaMGGCevTooUmTJp3SvHHyiH8R69Chg1599VV99dVXBR6ynD9/vtavXx9wWLxs2bLatWtXvnWPfNb+wQcfKDs7W++//37AHsGRh7JP1LECXa5cOV199dWaMmWKunfvrq+//lojR4487jarVaum3NxcrV69WnXr1vUvT09P165du1StWrWTnmfeedasWaNWrVr5lx86dEjr168P+IVZs2ZNff/992rduvVpeQJSoUIFlSpVqsC3nK1YsUIRERH5nlCcqpo1a+qzzz5TixYtTujtYU2bNlXTpk311FNPaerUqerevbumT58ecFi3qF177bXq06ePFi1apDfffPOo67Vv317PPPOMpkyZEhD/UOjSpYsGDRqkadOm+T8L48Ybbzzu+dq3b69FixZp5syZAYfMj6ZChQqKi4tTTk6Of285WHmPjZUrV/r/tJVn5cqVp/R4O5qaNWtqz549JzT3qKgodejQQR06dFBubq769eunV199VQ8//PBxj6jg9OCwfxH75z//qVKlSqlPnz7avn17wNiOHTt05513Kj4+XnfddZd/ec2aNZWRkRFwxGDTpk2aOXNmwPnznp0f/mw8IyNDEyZMOKW5xsbGSlKBTzwk398uf/nlFw0ePFglSpRQly5djrvNv//975KU74lC3p741VdffdLzvPjii5WYmKjx48fr0KFD/uVTpkzJd6i3c+fO+uuvvzR+/Ph829m/f7/27t17UpddokQJtWnTRu+9957Wr1/vX56enu7/UJv4+PiTu0JH0blzZ+Xk5OiJJ57IN3bo0CH/z2nnzp359vzyPho5mEP/p0Pp0qX1yiuv6NFHH1WHDh2Oul6LFi105ZVXaty4cXrvvfcKXCeYoy8no3z58mrXrp3eeOMNTZkyRVddddUJ/Umub9++qlSpku69916tWrUq3/iR8y9RooQ6deqkd955Rz/99FO+9bdu3XrSc7/44otVsWJFjR07NuBnn5aWpl9//fWUHm9H07lzZy1cuFCffPJJvrFdu3b5H5tH/t6LiIjwP0EP9f3TJez5F7FatWpp8uTJ6tq1q84///x8n/C3c+dOTZ8+PeADfrp06aIhQ4bo2muv1d133619+/bplVde0bnnnhvwAqA2bdr4n1H36dNHe/bs0fjx41WxYkVt2rTppOfaqFEjSdLdd9+ttm3b5gv81VdfrcTERM2YMUPt2rVTxYoVj7vNCy64QD179tS4ceO0a9cutWzZUosXL9akSZPUsWPHgD33ExUVFaVHH31UAwYM0BVXXKHOnTtr/fr1mjhxomrWrBmwh3/zzTfrrbfe0p133qkvvvhCLVq0UE5OjlasWKG33npLn3zyiS6++OKTuvwnn3xSn376qS699FL169dPkZGRevXVV5Wdna3nnnvupK/P0bRs2VJ9+vTR8OHDtXz5crVp00YlS5bU6tWrNWPGDL344ou6/vrrNWnSJL388su69tprVbNmTe3evVvjx49XfHy8/8lXKB3tkPaR3njjDV111VXq2LGj2rVrp9TUVJUtW9b/CX/z5s1Tu3btCnm2Pj169PC/PqGgJ18FKVeunGbOnKkOHTroggsuUJcuXdS4cWOVLFlSGzZs0IwZMyQF/t3+mWee0RdffKEmTZro9ttvV7169bRjxw4tW7ZMn332mXbs2HFS8y5ZsqSeffZZ9erVSy1btlTXrl2Vnp6uF198UdWrV9e99957Uts7lsGDB+v9999X+/btdcstt6hRo0bau3evfvzxR7399ttav369ypcvr9tuu007duzQFVdcoSpVquj333/X6NGj1bBhw4CjgShkIXmPAezHH3+0bt26WVJSkkVERJgki46Otp9//rnA9WfPnm3169e3qKgoq127tr3xxhsFvtXv/ffftwYNGvg/2OXZZ5/1f1jJunXr/OudyFv9Dh06ZAMGDLAKFSqYx+Mp8G1//fr1M0k2derUE77uBw8etMcee8xq1KhhJUuWtJSUlHwf8mN24m/1yzNq1CirVq2aeb1eu+SSS+zrr7+2Ro0a2VVXXRWw3oEDB+zZZ5+18847z7xer5UtW9YaNWpkjz32mGVkZPjX0///IT9HqlatmvXs2TNg2bJly6xt27ZWunRpK1WqlLVq1SrfB8Ac/ra3w+X9HLdu3XpC13/cuHHWqFEji4mJsbi4ODv//PPt/vvvt40bN/rn0rVrV6tatar/g4Dat29v33777XFvw6O91W/GjBkB6xV0fynI0a7zkQp6m52Z7619I0eOtGbNmll8fLxFRkZaUlKStW/f3qZMmRLwdrGTmeuJvtUvT94HEiUkJAS8Ze5EbNq0yQYPHmz16tWzmJgY83q9dvbZZ1uPHj0K/EyM9PR069+/v6WkpFjJkiUtKSnJWrdubePGjTul62pm9uabb9qFF15oXq/XypUrd8wP+TlSy5Yt7bzzzsu3vKDba/fu3TZ06FCrVauWRUVFWfny5a158+b2n//8x/95B2+//ba1adPG/yFGVatWtT59+timTZuOfUPitPKYFdGxMxzT5MmTdcstt+imm27S5MmTQz2dE3bvvffqtdde0+bNm1WqVKlQTydAbm6uKlSooOuuu67Aw/zAiTp06JCSk5PVoUMHvfbaa6GeDhA0DvsXEz169NCmTZv0wAMPqEqVKmHxH+yysrL0xhtvqFOnTiEPf1ZWlrxeb8Ah/smTJ2vHjh1O/ItaFK53331XW7duVY8ePUI9FeC0YM8fJ23Lli367LPP9Pbbb+vdd9/VsmXL/C8oC5Uvv/xS9957r2644QYlJiZq2bJleu2111S3bl0tXbqUfw6EU/LNN9/ohx9+0BNPPKHy5cvn+5AdIFyx54+T9ssvv6h79+6qWLGiRo0aFfLwS1L16tWVkpKiUaNGaceOHSpXrpx69OihZ555hvDjlL3yyit644031LBhQ02cODHU0wFOG/b8AQBwDO/zBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADH8D5/+T4GduPGjYqLiwvZ/5kHACBYZqbdu3crOTlZERFH378n/pI2btx42v7nOgAAobZhwwZVqVLlqOPEX1JcXJwk3411uv73OgAARS0zM1MpKSn+rh0N8Zf8h/rj4+OJPwAg7B3vT9i84A8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxIY3/vHnz1KFDByUnJ8vj8ejdd98NGDczDRs2TJUrV1ZMTIxSU1O1evXqgHV27Nih7t27Kz4+XmXKlFHv3r21Z8+eIrwWAACEl8hQXvjevXt1wQUX6NZbb9V1112Xb/y5557TqFGjNGnSJNWoUUMPP/yw2rZtq19++UXR0dGSpO7du2vTpk369NNPdfDgQfXq1Ut33HGHpk6dWtRXJ2yZmbKyskI9jRNmZsrOzpYkeb1eeTyeEM/oxERHR4fNXAGc2TxmZqGehCR5PB7NnDlTHTt2lOT7BZ+cnKz77rtP//znPyVJGRkZqlSpkiZOnKguXbro119/Vb169bRkyRJdfPHFkqRZs2bp73//u/78808lJycXeFnZ2dn+eEhSZmamUlJSlJGRofj4+MK9osXQ/v371a5du1BP44yXlpammJiYUE8DwBksMzNTCQkJx+1Zsf2b/7p167R582alpqb6lyUkJKhJkyZauHChJGnhwoUqU6aMP/ySlJqaqoiICH3zzTdH3fbw4cOVkJDg/0pJSSm8KwIAQDET0sP+x7J582ZJUqVKlQKWV6pUyT+2efNmVaxYMWA8MjJS5cqV869TkKFDh2rQoEH+03l7/q6Kjo5WWlpaqKdxwrKysnTttddKkmbOnOn/E1BxFy7zBHDmK7bxL0xer1derzfU0yg2PB5P2B6Ojo6ODtu5A0CoFNvD/klJSZKk9PT0gOXp6en+saSkJG3ZsiVg/NChQ9qxY4d/HQAAEKjYxr9GjRpKSkrSnDlz/MsyMzP1zTffqFmzZpKkZs2aadeuXVq6dKl/nc8//1y5ublq0qRJkc8ZAIBwENLD/nv27NGaNWv8p9etW6fly5erXLlyqlq1qu655x49+eSTOuecc/xv9UtOTva/I6Bu3bq66qqrdPvtt2vs2LE6ePCg7rrrLnXp0uWor/QHAMB1IY3/t99+q1atWvlP570Ir2fPnpo4caLuv/9+7d27V3fccYd27dqlSy+9VLNmzQp44dSUKVN01113qXXr1oqIiFCnTp00atSoIr8uAACEi2LzPv9QOtH3RaJ4OPxzCXjvPAD8T9i/zx8AABQO4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjgnpP/Y5U5mZsrKyQj2NM9bhty23c+GJjo6Wx+MJ9TQAFALiXwiysrL8/3gGhevaa68N9RTOWPzTJODMxWF/AAAcw55/IdvTsKssgpv5tDKTcg/5vo+IlDg0fdp4cg+p9PJpoZ4GgEJGlQqZRURKJUqGehpnoKhQT+CMZKGeAIAiwWF/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHBMZKgncCYys/+dyDkYuokAJ+uw+2vA/RjAGYX4F4Ls7Gz/93HfTw/hTIBTl52drVKlSoV6GgAKAYf9AQBwDHv+hcDr9fq/331BF6lEyRDOBjgJOQf9R6sOvx8DOLMQ/0Lg8Xj+d6JESeKPsBRwPwZwRuGwPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOCYyFBP4EznyT0kC/UkzjRmUu4h3/cRkZLHE9r5nEE8ebcrgDMa8S9kpZdPC/UUAAAIwGF/AAAcw55/IYiOjlZaWlqop3HGysrK0rXXXitJmjlzpqKjo0M8ozMTtytw5iL+hcDj8SgmJibU03BCdHQ0tzUAnCQO+wMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4JhiHf+cnBw9/PDDqlGjhmJiYlSzZk098cQTMjP/OmamYcOGqXLlyoqJiVFqaqpWr14dwlkDAFC8Fev4P/vss3rllVf00ksv6ddff9Wzzz6r5557TqNHj/av89xzz2nUqFEaO3asvvnmG8XGxqpt27bKysoK4cwBACi+IkM9gWNZsGCBrrnmGl199dWSpOrVq2vatGlavHixJN9e/8iRI/XQQw/pmmuukSRNnjxZlSpV0rvvvqsuXbqEbO4AABRXxXrPv3nz5pozZ45WrVolSfr+++/11VdfqV27dpKkdevWafPmzUpNTfWfJyEhQU2aNNHChQuPut3s7GxlZmYGfAEA4Ipivef/wAMPKDMzU3Xq1FGJEiWUk5Ojp556St27d5ckbd68WZJUqVKlgPNVqlTJP1aQ4cOH67HHHiu8iQMAUIwV6z3/t956S1OmTNHUqVO1bNkyTZo0Sf/5z380adKkoLY7dOhQZWRk+L82bNhwmmYMAEDxV6z3/AcPHqwHHnjA/7f7888/X7///ruGDx+unj17KikpSZKUnp6uypUr+8+Xnp6uhg0bHnW7Xq9XXq+3UOcOAEBxVaz3/Pft26eIiMAplihRQrm5uZKkGjVqKCkpSXPmzPGPZ2Zm6ptvvlGzZs2KdK4AAISLYr3n36FDBz311FOqWrWqzjvvPH333Xd64YUXdOutt0qSPB6P7rnnHj355JM655xzVKNGDT388MNKTk5Wx44dQzt5AACKqWId/9GjR+vhhx9Wv379tGXLFiUnJ6tPnz4aNmyYf537779fe/fu1R133KFdu3bp0ksv1axZsxQdHR3CmQMAUHx57PCPy3NUZmamEhISlJGRofj4+FBPB8exf/9+/9s909LSFBMTE+IZAUDxcKI9K9Z/8wcAAKcf8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wR1q677rpQTwEAwg7xR9hZvHix//v9+/dr0aJFIZwNAIQfj5lZqCcRapmZmUpISFBGRobi4+NDPZ0iZ2bKysoK9TROWLt27fItS0tLC8FMTk50dLQ8Hk+opwHgDHaiPYsswjmhmMrKyiowqOEkHOaflpammJiYUE8DADjsDwCAa9jzh6Kjo8PisPnAgQO1atWqo46fe+65evHFF4twRicnOjo61FMAAEnEH5I8Hk9YHI7Oyck57ng4XA8ACDUO+yNsREYe+7nq8cYBAD7EH2GjQYMGQY0DAHyIP8LG8d6VyrtWAeDEEH+Eja+//jqocQCAD/FH2KhWrVpQ4wAAH+KPsLFr166gxgEAPsQfYeO3334LahwA4EP8ETY6dOgQ1DgAwIf4I2ykp6cHNQ4A8CH+CBtxcXFBjQMAfIg/wgbxB4DTg/gjbGzatCmocQCAD/FH2Dhw4EBQ4wAAH+KPsLFly5agxgEAPsQfYSMjIyOocQCAD/FH2LjooouCGgcA+BB/hI1t27YFNQ4A8CH+CBt16tQJahwA4EP8ETY8Hk9Q4wAAH+KPsBEfHx/UOADAh/gjbMyePTuocQCAD/FH2PB6vUGNAwB8iD/CBi/4A4DTg/gjbKxevTqocQCAD/FH2OBDfgDg9CD+CBszZswIahwA4EP8ETYqVKgQ1DgAwIf4I2zUrl07qHEAgA/xR9jYunVrUOMAAB/ij7ARGxsb1DgAwIf4I2zs2LEjqHEAgA/xR9ioV69eUOMAAB/ij7CxfPnyoMYBAD7EH2GjYcOGQY0DAHyIP8LGt99+G9Q4AMCH+CNsbNy4MahxAIAP8UfYaN++fVDjAAAf4o+wkZ2dHdQ4AMCH+CNs5ObmBjUOAPAh/ggbGzZsCGocAOBD/BE2rrvuuqDGAQA+xB9h4/PPPw9qHADgQ/wRNv7444+gxgEAPsQfYaNx48ZBjQMAfIg/wgZ7/gBwehB/hI0KFSoENQ4A8CH+CBvx8fFBjQMAfIg/wsYPP/wQ1DgAwIf4I2xUr149qHEAgE+xj/9ff/2lm266SYmJiYqJidH5558f8K9bzUzDhg1T5cqVFRMTo9TUVK1evTqEM0ZhOXToUFDjAACfYh3/nTt3qkWLFipZsqTS0tL0yy+/6Pnnn1fZsmX96zz33HMaNWqUxo4dq2+++UaxsbFq27atsrKyQjhzFIb169cHNQ4A8IkM9QSO5dlnn1VKSoomTJjgX1ajRg3/92amkSNH6qGHHtI111wjSZo8ebIqVaqkd999V126dCnyOaPwXHHFFfrtt9+OOQ4AOL5ivef//vvv6+KLL9YNN9ygihUr6sILL9T48eP94+vWrdPmzZuVmprqX5aQkKAmTZpo4cKFR91udna2MjMzA75Q/EVEHPvuerxxAIBPsf5tuXbtWr3yyis655xz9Mknn6hv3766++67NWnSJEnS5s2bJUmVKlUKOF+lSpX8YwUZPny4EhIS/F8pKSmFdyVw2nz66adBjQMAfIp1/HNzc3XRRRfp6aef1oUXXqg77rhDt99+u8aOHRvUdocOHaqMjAz/F/8KNjx069YtqHEAgE+xjn/lypVVr169gGV169b1f4xrUlKSJCk9PT1gnfT0dP9YQbxer+Lj4wO+UPwtXbo0qHEAgE+xjn+LFi20cuXKgGWrVq1StWrVJPle/JeUlKQ5c+b4xzMzM/XNN9+oWbNmRTpXFL6DBw8GNQ4A8CnWr/a/99571bx5cz399NPq3LmzFi9erHHjxmncuHGSJI/Ho3vuuUdPPvmkzjnnHNWoUUMPP/ywkpOT1bFjx9BOHqfd/PnzgxoHAPgU6z3/xo0ba+bMmZo2bZrq16+vJ554QiNHjlT37t3969x///0aMGCA7rjjDjVu3Fh79uzRrFmzFB0dHcKZozBcddVVQY0DAHyK9Z6/JLVv317t27c/6rjH49Hjjz+uxx9/vAhnhVCIiYkJahwA4FOs9/yBw5UrVy6ocQCAD/FH2NixY0dQ4wAAH+IPAIBjiD/CRmJiYlDjAAAf4o+wsXHjxqDGAQA+xB9h4/APczqVcQCAD/FH2LjkkkuCGgcA+BB/hI3jfWQzH+kMACeG+CNsvPPOO0GNAwB8iD/CRp06dYIaBwD4EH+EjX379gU1DgDwIf4IG2vXrg1qHADgQ/wRNo73nxr5T44AcGKIP8IGf/MHgNOD+CNslC1bNqhxAIAP8UfY+OSTT4IaBwD4EH+EjYSEhKDGAQA+xB9hg0/4A4DTg/gjbOzYsSOocQCAD/FH2Fi6dGlQ4wAAH+KPsNG1a9egxgEAPsQfYSMjIyOocQCAD/FH2KhUqVJQ4wAAH+KPsHHJJZcENQ4A8CH+CBuTJk0KahwA4EP8ETYOHToU1DgAwIf4I2xERBz77nq8cQCAD78tETbMLKhxAIAP8UfYKFeuXFDjAAAf4o+wwcf7AsDpQfwRNjIzM4MaBwD4EH+EjaZNmwY1DgDwIf4IG8QfAE4P4o+wMXHixKDGAQA+xB9hY8OGDUGNAwB8iD/CRv369YMaBwD4EH+EjZ07dwY1DgDwIf4IG3y8LwCcHvy2RNho1apVUOMAAB/ij7Dx119/BTUOAPAh/ggbc+bMCWocAOBD/BE2oqOjgxoHAPgQf4QN/rEPAJwexB9h4/bbbw9qHADgQ/wRNn799degxgEAPsQfYaNOnTpBjQMAfIg/wsbo0aODGgcA+BB/hI3LL788qHEAgA/xR9jgH/sAwOlB/BE2XnrppaDGAQA+xB9ho0GDBkGNAwB8iD/CRq9evYIaBwD4EH+EjY8++iiocQCAD/FH2DCzoMYBAD7EH2Hj/PPPD2ocAOBD/BE25s6dG9Q4AMCH+CNslC1bNqhxAIAP8UfY4EN+AOD0IP4IG2+++WZQ4wAAH+KPsDFkyJCgxgEAPsQfYWPZsmVBjQMAfIg/wkaFChWCGgcA+BB/hI133303qHEAgA/xR9goVapUUOMAAB/ij7CRnp4e1DgAwIf4I2zceOONQY0DAHyIP8LGpEmTghoHAPgQf4SNxx57LKhxAIAP8UfY+O6774IaBwD4EH+EjcTExKDGAQA+xB9hY8qUKUGNAwB8iD/CxllnnRXUOADAh/gjbPTq1SuocQCAD/FH2Pjmm2+CGgcA+AQd/23btumjjz7S+++/r02bNp2OOQEF+vXXX4MaBwD4RAZz5nfeeUe9e/fWueeeq4MHD2rlypUaM2YMh19RKMwsqHEAgM9J7fnv2bMn4PRjjz2mxYsXa/Hixfruu+80Y8YM/etf/zqtEwTy8FY/ADg9Tir+jRo10nvvvec/HRkZqS1btvhPp6enKyoq6vTNDjjMhRdeGNQ4AMDnpA77f/LJJ+rfv78mTpyoMWPG6MUXX9SNN96onJwcHTp0SBEREZo4cWIhTRWuK1GiRFDjAACfk4p/9erV9dFHH2natGlq2bKl7r77bq1Zs0Zr1qxRTk6O6tSpo+jo6MKaKxxXrVo1nX/++frxxx/zjTVo0EDVqlULwawAIPyc0qv9u3btqiVLluj777/X5ZdfrtzcXDVs2JDwo1B5PB4NGTKkwLEhQ4bI4/EU8YwAIDyddPw//vhjPf/88/r222/1//7f/9Nzzz2n7t27a/Dgwdq/f39hzBHwq1Klirp27RqwrFu3bny6HwCchJOK/3333adevXppyZIl6tOnj5544gm1bNlSy5YtU3R0tC688EKlpaUV1lwBSVLPnj0VFxcnSYqPj1ePHj1CPCMACC8eO4k3RycmJmr27Nlq1KiRduzYoaZNm2rVqlX+8V9++UV9+vTR/PnzC2WyhSUzM1MJCQnKyMhQfHx8qKeDE7BgwQK9+OKLGjhwoJo3bx7q6QBAsXCiPTupF/zFxsZq3bp1atSokTZs2JDvb/z16tULu/AjPDVv3pzoA8ApOqnD/sOHD1ePHj2UnJysli1b6oknniiseQEAgEJyUof9JWn79u1au3atzjnnHJUpU6aQplW0OOwPADgTFMphf8n3d38+RhUAgPDFv/QFAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwTFjF/5lnnpHH49E999zjX5aVlaX+/fsrMTFRpUuXVqdOnZSenh66SQIAUMyFTfyXLFmiV199VQ0aNAhYfu+99+qDDz7QjBkzNHfuXG3cuFHXXXddiGYJAEDxFxbx37Nnj7p3767x48erbNmy/uUZGRl67bXX9MILL+iKK65Qo0aNNGHCBC1YsECLFi0K4YwBACi+wiL+/fv319VXX63U1NSA5UuXLtXBgwcDltepU0dVq1bVwoULj7q97OxsZWZmBnwBAOCKyFBP4HimT5+uZcuWacmSJfnGNm/erKioKJUpUyZgeaVKlbR58+ajbnP48OF67LHHTvdUAQAIC8V6z3/Dhg0aOHCgpkyZoujo6NO23aFDhyojI8P/tWHDhtO2bQAAirtiHf+lS5dqy5YtuuiiixQZGanIyEjNnTtXo0aNUmRkpCpVqqQDBw5o165dAedLT09XUlLSUbfr9XoVHx8f8AUAgCuK9WH/1q1b68cffwxY1qtXL9WpU0dDhgxRSkqKSpYsqTlz5qhTp06SpJUrV+qPP/5Qs2bNQjFlAACKvWId/7i4ONWvXz9gWWxsrBITE/3Le/furUGDBqlcuXKKj4/XgAED1KxZMzVt2jQUUwYAoNgr1vE/ESNGjFBERIQ6deqk7OxstW3bVi+//HKopwUAQLHlMTML9SRCLTMzUwkJCcrIyODv/wCAsHWiPSvWL/gDAACnH/EHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHEP8AQBwDPEHAMAxxB8AAMcQfwAAHFOs4z98+HA1btxYcXFxqlixojp27KiVK1cGrJOVlaX+/fsrMTFRpUuXVqdOnZSenh6iGQMAUPwV6/jPnTtX/fv316JFi/Tpp5/q4MGDatOmjfbu3etf595779UHH3ygGTNmaO7cudq4caOuu+66EM4aAIDizWNmFupJnKitW7eqYsWKmjt3rv72t78pIyNDFSpU0NSpU3X99ddLklasWKG6detq4cKFatq06QltNzMzUwkJCcrIyFB8fHxhXgUAAArNifasWO/5HykjI0OSVK5cOUnS0qVLdfDgQaWmpvrXqVOnjqpWraqFCxcedTvZ2dnKzMwM+AIAwBVhE//c3Fzdc889atGiherXry9J2rx5s6KiolSmTJmAdStVqqTNmzcfdVvDhw9XQkKC/yslJaUwpw4AQLESNvHv37+/fvrpJ02fPj3obQ0dOlQZGRn+rw0bNpyGGQIAEB4iQz2BE3HXXXfpww8/1Lx581SlShX/8qSkJB04cEC7du0K2PtPT09XUlLSUbfn9Xrl9XoLc8oAABRbxXrP38x01113aebMmfr8889Vo0aNgPFGjRqpZMmSmjNnjn/ZypUr9ccff6hZs2ZFPV0AAMJCsd7z79+/v6ZOnar33ntPcXFx/r/jJyQkKCYmRgkJCerdu7cGDRqkcuXKKT4+XgMGDFCzZs1O+JX+AAC4pli/1c/j8RS4fMKECbrlllsk+T7k57777tO0adOUnZ2ttm3b6uWXXz7mYf8j8VY/AMCZ4ER7VqzjX1SIPwDgTHBGvs8fAAAEj/gDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4JjLUEwAAFE+XX365//svv/wyZPPA6ceePwAgn0ceeeSYpxHeiD8AIJ+5c+ce8zTCG/EHAARo1arVSS1H+CH+AAC/TZs2ycwKHDMzbdq0qYhnhMJA/AEAfl27dg1qHOGB+AMA4BjiDwCAY4g/AACOIf4AADiG+AMA4BjiDwCAY/hsfwAoAmamrKysUE/jtNi/f3+op3BU0dHR8ng8oZ5GsUf8AaAIZGVlqV27dqGexmlRnK9HWlqaYmJiQj2NYo/D/gAAOIY9fwAoAtHR0UpLSwv1NI5r9+7d6ty581HH33rrLcXFxRXhjE5OdHR0qKcQFog/ABQBj8cTFoejY2JilJSUpM2bN+cbq1y5sipWrBiCWeF047A/ACDA9OnTC1w+bdq0Ip4JCgvxBwDk06dPn4DTAwYMCNFMUBiIPwAgn44dOwac7tSpU2gmgkJB/AEAxxQOL1TEySH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGOIPwAAjiH+AAA4hvgDAOAY4g8AgGMiQz0BADgVZqasrKxQT+OMdfhty+1ceKKjo+XxeIr8cok/gLCUlZWldu3ahXoaTrj22mtDPYUzVlpammJiYor8cjnsDwCAY9jzBxD2Xrp0h7wlLNTTOKOYSQdyfd9HRUghODJ9xsrO8eiur8qFdA7EH0DY85YweUuEehZnnuhQT+CMFfonqhz2BwDAMcQfAADHcNgfQFgy+9+h0+ycEE4EOEmH318Pvx8XJeIPICxlZ2f7v7/rq8QQzgQ4ddnZ2SpVqlSRXy6H/QEAcAx7/gDCktfr9X//0qXbebU/wkZ2zv+OVh1+Py5KxB9AWDr8I1G9JUT8EZZC8dG+0hl02H/MmDGqXr26oqOj1aRJEy1evDjUUwIAoFg6I/b833zzTQ0aNEhjx45VkyZNNHLkSLVt21YrV65UxYoVQz09AIUsO8ej4vDBKcdy+CfmofCEw6cR+u6voeWxUL3P4DRq0qSJGjdurJdeekmSlJubq5SUFA0YMEAPPPBAvvWzs7MDXimcmZmplJQUZWRkKD4+vsjmDeDU7d+/n3/sg7B3uv+xT2ZmphISEo7bs7A/7H/gwAEtXbpUqamp/mURERFKTU3VwoULCzzP8OHDlZCQ4P9KSUkpqukCABByYX/Yf9u2bcrJyVGlSpUClleqVEkrVqwo8DxDhw7VoEGD/Kfz9vwBhI/o6GilpaWFehonzMwCjjiicHi93pC9iO5UREeH5j8ohH38T4XX6w3Z2ysAnB4ejyck/wc9GKH4MBegIGF/2L98+fIqUaKE0tPTA5anp6crKSkpRLMCAKD4Cvv4R0VFqVGjRpozZ45/WW5urubMmaNmzZqFcGYAABRPZ8Rh/0GDBqlnz566+OKLdckll2jkyJHau3evevXqFeqpAQBQ7JwR8b/xxhu1detWDRs2TJs3b1bDhg01a9asfC8CBAAAZ8j7/IN1ou+LBACgOHPmff4AAODkEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABxD/AEAcAzxBwDAMcQfAADHEH8AABwTGeoJFAdmJknKzMwM8UwAADh1eR3L69rREH9Ju3fvliSlpKSEeCYAAARv9+7dSkhIOOq4x4739MABubm52rhxo+Li4uTxeEI9HZyAzMxMpaSkaMOGDYqPjw/1dIAzEo+z8GNm2r17t5KTkxURcfS/7LPnLykiIkJVqlQJ9TRwCuLj4/mlBBQyHmfh5Vh7/Hl4wR8AAI4h/gAAOIb4Iyx5vV498sgj8nq9oZ4KcMbicXbm4gV/AAA4hj1/AAAcQ/wBAHAM8QcAwDHEHwAAxxD/M8zEiRNVpkwZ/+lHH31UDRs2DNl8zhTVq1fXyJEjQz0NhBiPr+KlqB6X69evl8fj0fLlywv9sooK8S9GjnYHu+WWW9SxY8cT2saNN96oVatWnfa5Eb+TQxSKHx5fha+o7/dLlizRHXfccVq3WdD9ISUlRZs2bVL9+vVP62WFEh/vewY5ePCgYmJiFBMTE+qpAGccHl/FT4UKFYrkckqUKKGkpKQiuawiYyhSaWlp1qJFC0tISLBy5crZ1VdfbWvWrDEzM0kBXy1btrRHHnkk3/IvvvjC1q1bZ5Js+vTp9re//c28Xq9NmDDBJkyYYAkJCf7Le+SRR+yCCy6wsWPHWpUqVSwmJsZuuOEG27Vrl3+dli1b2sCBAwPmec0111jPnj3940fOIc/8+fPt0ksvtejoaKtSpYoNGDDA9uzZ4x8fM2aM1apVy7xer1WsWNE6dep0zNvnq6++spYtW1pMTIyVKVPG2rRpYzt27DAzs6ysLBswYIBVqFDBvF6vtWjRwhYvXuw/7xdffGGSbNasWdawYUOLjo62Vq1aWXp6un388cdWp04di4uLs65du9revXsDrn///v2tf//+Fh8fb4mJifbQQw9Zbm6uf51q1arZiBEj/Kd37txpvXv3tvLly1tcXJy1atXKli9fbmZmEyZMyHd7TZgw4bjnO/znNXnyZKtWrZrFx8fbjTfeaJmZmf51cnJy7Omnn7bq1atbdHS0NWjQwGbMmOEf37Fjh3Xr1s3Kly9v0dHRVqtWLfvvf/9rZmbZ2dnWv39/S0pKMq/Xa1WrVrWnn376mD+TcMLjq/AfX5999pk1atTIYmJirFmzZrZixQozO/b9/vnnn7f69etbqVKlrEqVKta3b1/bvXu3f9t5t+sHH3xg5557rsXExFinTp1s7969NnHiRKtWrZqVKVPGBgwYYIcOHfKf78jHpSQbP368dezY0WJiYqxWrVr23nvv+ccPHTpkt956q/+xc+6559rIkSMDfp7Huj989913/nW//PJLa9y4sUVFRVlSUpINGTLEDh48GPBzHzBggA0ePNjKli1rlSpVskceeeSYP5+iRPyL2Ntvv23vvPOOrV692r777jvr0KGDnX/++ZaTk2OLFy/2P7g2bdpk27dvt927d1vnzp3tqquusk2bNtmmTZssOzvbf2esXr26vfPOO7Z27VrbuHFjgb+cYmNj7YorrrDvvvvO5s6da7Vq1bJu3br51zneL6ft27dblSpV7PHHH/fPwcxszZo1FhsbayNGjLBVq1bZ119/bRdeeKHdcsstZma2ZMkSK1GihE2dOtXWr19vy5YtsxdffPGot813331nXq/X+vbta8uXL7effvrJRo8ebVu3bjUzs7vvvtuSk5Pt448/tp9//tl69uxpZcuWte3bt5vZ/345NW3a1L766itbtmyZ1apVy1q2bGlt2rSxZcuW2bx58ywxMdGeeeaZgOtfunRpGzhwoK1YscLeeOMNK1WqlI0bN86/zpG/ZFJTU61Dhw62ZMkSW7Vqld13332WmJho27dvt3379tl9991n5513nv/22rdv33HPl/fzKl26tF133XX2448/2rx58ywpKckefPBB/2U/+eSTVqdOHZs1a5b99ttvNmHCBPN6vfbll1+amVn//v2tYcOGtmTJElu3bp19+umn9v7775uZ2b///W9LSUmxefPm2fr1623+/Pk2derUo/5Mwg2Pr8J/fDVp0sS+/PJL+/nnn+2yyy6z5s2bm5kd834/YsQI+/zzz23dunU2Z84cq127tvXt29c/twkTJljJkiXtyiuvtGXLltncuXMtMTHR2rRpY507d7aff/7ZPvjgA4uKirLp06f7z1dQ/KtUqWJTp0611atX2913322lS5f2X4cDBw7YsGHDbMmSJbZ27Vr/Y/3NN980Mzvu/SEv/n/++aeVKlXK+vXrZ7/++qvNnDnTypcvHxD3li1bWnx8vD366KO2atUqmzRpknk8Hps9e/ZRf0ZFifiH2NatW02S/fjjjwU+uzQz69mzp11zzTUBy/LWPfxZq5kV+MupRIkS9ueff/qXpaWlWUREhP+XzPF+OZnlf5CZmfXu3dvuuOOOgGXz58+3iIgI279/v73zzjsWHx8fsNd6LF27drUWLVoUOLZnzx4rWbKkTZkyxb/swIEDlpycbM8995yZBe6Z5Bk+fLhJst9++82/rE+fPta2bVv/6ZYtW1rdunUD9vSHDBlidevWLfD6z58/3+Lj4y0rKytgjjVr1rRXX33VzP63R3i4Ez1fqVKlAm6zwYMHW5MmTczMt3dWqlQpW7BgQcA2evfubV27djUzsw4dOlivXr3y3YZmZgMGDLArrrgi4LqeyXh8/U9hPL4++ugjk2T79+/33x5H3u8LMmPGDEtMTPSfzjtqkHeUxsz3OC1VqlTAEYK2bdtanz59/KcLiv9DDz0UcL0kWVpa2lHn0r9//4AjJse6P+Tddx588EGrXbt2wONozJgxVrp0acvJyTEz38/90ksvDdhO48aNbciQIUedS1HiBX9FbPXq1eratavOPvtsxcfHq3r16pKkP/7445S2d/HFFx93napVq+qss87yn27WrJlyc3O1cuXKU7rMPN9//70mTpyo0qVL+7/atm2r3NxcrVu3TldeeaWqVaums88+WzfffLOmTJmiffv2HXV7y5cvV+vWrQsc++2333Tw4EG1aNHCv6xkyZK65JJL9Ouvvwas26BBA//3lSpVUqlSpXT22WcHLNuyZUvAeZo2bSqPx+M/3axZM61evVo5OTkFXu89e/YoMTEx4LqvW7dOv/3221Gv34mer3r16oqLi/Ofrly5sn++a9as0b59+3TllVcGbGPy5Mn+bfTt21fTp09Xw4YNdf/992vBggX+bd1yyy1avny5ateurbvvvluzZ88+6nzDEY+von18Va5cWZLyPZ6O9Nlnn6l169Y666yzFBcXp5tvvlnbt28PmG+pUqVUs2ZN/+lKlSqpevXqKl26dMCy413W4fOLjY1VfHx8wHnGjBmjRo0aqUKFCipdurTGjRt30vePX3/9Vc2aNQv4ndGiRQvt2bNHf/75Z4FzkQIfy6HGC/6KWIcOHVStWjWNHz9eycnJys3NVf369XXgwIFT2l5sbGzQc4qIiJAd8S8eDh48eNzz7dmzR3369NHdd9+db6xq1aqKiorSsmXL9OWXX2r27NkaNmyYHn30US1ZsiTg7VJ5TtcLqUqWLOn/3uPxBJzOW5abm3vK29+zZ48qV66sL7/8Mt9YQdfrZM93rPnu2bNHkvTRRx8FBEeS/5+vtGvXTr///rs+/vhjffrpp2rdurX69++v//znP7rooou0bt06paWl6bPPPlPnzp2Vmpqqt99++wSuefHH46voH1+Sjvl4Wr9+vdq3b6++ffvqqaeeUrly5fTVV1+pd+/eOnDggEqVKpVvu3nbPpXH7rHOM336dP3zn//U888/r2bNmikuLk7//ve/9c033xznWp+a0/2753Riz78Ibd++XStXrtRDDz2k1q1bq27dutq5c6d/PCoqSpLy7W1GRUUVuAd6ov744w9t3LjRf3rRokWKiIhQ7dq1JfleMbtp0yb/eE5Ojn766afjzuGiiy7SL7/8olq1auX7yrsukZGRSk1N1XPPPacffvhB69ev1+eff17gPBs0aKA5c+YUOFazZk1FRUXp66+/9i87ePCglixZonr16p3ErVGwIx/8ixYt0jnnnKMSJUrkW/eiiy7S5s2bFRkZme96ly9fXtLRb6/jne946tWrJ6/Xqz/++CPfNlJSUvzrVahQQT179tQbb7yhkSNHaty4cf6x+Ph43XjjjRo/frzefPNNvfPOO9qxY8cJ31bFFY+v0D++CroeS5cuVW5urp5//nk1bdpU5557bsDtVZS+/vprNW/eXP369dOFF16oWrVq5TtadyL3h7p162rhwoUBT+q+/vprxcXFqUqVKoUy99ON+BehsmXLKjExUePGjdOaNWv0+eefa9CgQf7xihUrKiYmRrNmzVJ6eroyMjIk+Q4D//DDD1q5cqW2bdt2QnsNh4uOjlbPnj31/fffa/78+br77rvVuXNn/1tXrrjiCn300Uf66KOPtGLFCvXt21e7du0K2Eb16tU1b948/fXXX9q2bZskaciQIVqwYIHuuusuLV++XKtXr9Z7772nu+66S5L04YcfatSoUVq+fLl+//13TZ48Wbm5uf5fii+99FLAYcihQ4dqyZIl6tevn3744QetWLFCr7zyirZt26bY2Fj17dtXgwcP1qxZs/TLL7/o9ttv1759+9S7d++T+0EU4I8//tCgQYO0cuVKTZs2TaNHj9bAgQMLXDc1NVXNmjVTx44dNXv2bK1fv14LFizQv/71L3377bf+22vdunVavny5tm3bpuzs7BM63/HExcXpn//8p+69915NmjRJv/32m5YtW6bRo0dr0qRJkqRhw4bpvffe05o1a/Tzzz/rww8/VN26dSVJL7zwgqZNm6YVK1Zo1apVmjFjhpKSko55xCJc8PgK/eOroPt9rVq1dPDgQY0ePVpr167V66+/rrFjx57UbXy6nHPOOfr222/1ySefaNWqVXr44Ye1ZMmSfNfhePeHfv36acOGDRowYIBWrFih9957T4888ogGDRqkiIgwyWqIX3PgnE8//dTq1q1rXq/XGjRoYF9++aVJspkzZ5qZ2fjx4y0lJcUiIiKsZcuWZma2ZcsWu/LKK6106dLHfOuJWcEvSLrgggvs5ZdftuTkZIuOjrbrr7/e//YeM98Le/r27WvlypWzihUr2vDhw/O9IGnhwoXWoEED83q9AW9FWrx4sX9usbGx1qBBA3vqqafMzPfipJYtW1rZsmUtJibGGjRo4H9Vbd7cqlWrFjD/L7/80po3b25er9fKlCljbdu2tZ07d5qZ2f79+23AgAFWvnz5Y74VKW/9gm6Pw2+TPC1btrR+/frZnXfeafHx8Va2bFl78MEHj/lWv8zMTBswYIAlJydbyZIlLSUlxbp3725//PGHmflemNepUycrU6ZMwFuejne+gl4wNWLEiIDbKTc310aOHGm1a9e2kiVLWoUKFaxt27Y2d+5cMzN74oknrG7duhYTE2PlypWza665xtauXWtmZuPGjbOGDRtabGysxcfHW+vWrW3ZsmV2puDxVbSPr++++84k2bp168zs6Pf7F154wSpXrmwxMTHWtm1bmzx5csC2TuRxapb/xXgFveAv72edJyEhwT+PrKwsu+WWWywhIcHKlCljffv2tQceeCDgck70/nAib/U73gs9Q8ljdsQfowDHXH755WrYsOEZ8QlrAHAiwuT4BAAAOF2IPwAAjuGwPwAAjmHPHwAAxxB/AAAcQ/wBAHAM8QcAwDHEHwAAxxB/AAAcQ/wBAHAM8QcAwDH/H6EQrfLaj1/KAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(5, 10), layout=\"constrained\") # set the size and layout\n", "spec = fig.add_gridspec(1, 1) # set the grid\n", @@ -5214,7 +2439,7 @@ }, { "cell_type": "code", - "execution_count": 360, + "execution_count": null, "id": "f587a45e-c800-4b5a-9580-5847632cb0c2", "metadata": { "tags": [] @@ -5236,24 +2461,12 @@ }, { "cell_type": "code", - "execution_count": 364, + "execution_count": null, "id": "6cc2ebaa-d2a4-4a37-8e11-8708a28e1406", "metadata": { "tags": [] }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name '______' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[364], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m______\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_____________________\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m mgnify:\n\u001b[1;32m 2\u001b[0m resources \u001b[38;5;241m=\u001b[39m __________________\n\u001b[1;32m 3\u001b[0m resources_df \u001b[38;5;241m=\u001b[39m __________________________\n", - "\u001b[0;31mNameError\u001b[0m: name '______' is not defined" - ] - } - ], + "outputs": [], "source": [ "with ______(\"_____________________\") as mgnify:\n", " resources = __________________\n", @@ -5302,24 +2515,12 @@ }, { "cell_type": "code", - "execution_count": 371, + "execution_count": null, "id": "983ca9d7-1c11-4054-a7f6-ce745763e053", "metadata": { "tags": [] }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name '_________________________' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[371], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Extract the 'attributes.name' and 'attributes.genome-count' columns from the resources_df dataframe\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m catalogue_names \u001b[38;5;241m=\u001b[39m \u001b[43m_________________________\u001b[49m\n\u001b[1;32m 3\u001b[0m genome_counts \u001b[38;5;241m=\u001b[39m __________________________\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Create the pie chart\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name '_________________________' is not defined" - ] - } - ], + "outputs": [], "source": [ "# Fill in the blanks in this code to generate the pie chart\n", "\n", @@ -5348,7 +2549,7 @@ }, { "cell_type": "code", - "execution_count": 370, + "execution_count": null, "id": "f2593f45-a17b-4342-94ad-5b5b439ff016", "metadata": { "jupyter": { @@ -5356,18 +2557,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Solution\n", "# Extract the 'attributes.name' and 'attributes.genome-count' columns\n", @@ -5426,25 +2616,12 @@ }, { "cell_type": "code", - "execution_count": 372, + "execution_count": null, "id": "16381adf-54d1-4c6f-a11b-6cf6035a30c5", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: MGYA00646965\n", - "Downloading file for MGYA00646965\n", - "Finished for MGYA00646965\n", - "Processing: MGYA00646967\n", - "Downloading file for MGYA00646967\n", - "Finished for MGYA00646967\n" - ] - } - ], + "outputs": [], "source": [ "endpoint = \"___________________\" \n", "\n", @@ -5473,7 +2650,7 @@ }, { "cell_type": "code", - "execution_count": 378, + "execution_count": null, "id": "8f5821ed-268a-4030-a2a5-e5d709e28b56", "metadata": { "jupyter": { @@ -5481,16 +2658,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading the file\n", - "Finished downloading MGYG000321626.faa\n" - ] - } - ], + "outputs": [], "source": [ "endpoint = \"genomes/MGYG000321626/downloads\" # we are only interested in a specific genome\n", "\n", From 001a2ab93e1cbc980bfdb10f00b88aabe9a9a00e Mon Sep 17 00:00:00 2001 From: Tatiana Gurbich Date: Thu, 3 Aug 2023 15:31:04 +0100 Subject: [PATCH 3/4] Cleaned execution printouts --- .../GSC23 MGnify Workshop.ipynb | 1209 +---------------- 1 file changed, 18 insertions(+), 1191 deletions(-) diff --git a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb index 9e5f424..472dd12 100644 --- a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb +++ b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ba4e90f6-7778-4d03-ae4f-05711d5d5c63", "metadata": { "tags": [] @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "80005880-ec79-4991-96b0-b1d8848eac99", "metadata": { "tags": [] @@ -219,27 +219,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d1661765-bdf8-44b5-9c10-db0ffe7fb4d5", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Type a Study Accession [default: MGYS00001935] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using \"MGYS00001935\" as Study Accession\n" - ] - } - ], + "outputs": [], "source": [ "from lib.variable_utils import get_variable_from_link_or_input\n", "\n", @@ -261,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "5173ced5-e607-49a2-88d1-88c57d68baaf", "metadata": { "tags": [] @@ -287,220 +272,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f1071967-42f7-4890-99dd-58d138ebffac", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.accessionattributes.analysis-statusattributes.experiment-typeattributes.analysis-summaryattributes.pipeline-versionattributes.is-privateattributes.complete-timeattributes.instrument-platformattributes.instrument-modelrelationships.sample.data.idrelationships.sample.data.typerelationships.run.data.idrelationships.run.data.typerelationships.study.data.idrelationships.study.data.type
0analysis-jobsMGYA00585242MGYA00585242completedamplicon[{'key': 'Submitted nucleotide sequences', 'value': '1474912'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after length filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '335618'}, {'key': 'Predicted SSU sequences', 'value': '335119'}, {'key': 'Predicted LSU sequences', 'value': '0'}]5.0False2021-07-23T09:20:39ILLUMINAIllumina HiSeq 2500ERS1871389samplesERR2098577runsMGYS00001935studies
1analysis-jobsMGYA00585243MGYA00585243completedamplicon[{'key': 'Submitted nucleotide sequences', 'value': '1178982'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1060738'}, {'key': 'Predicted SSU sequences', 'value': '1049588'}, {'key': 'Predicted LSU sequences', 'value': '0'}]5.0False2021-07-23T09:22:00ILLUMINAIllumina HiSeq 2500ERS1871409samplesERR2098437runsMGYS00001935studies
2analysis-jobsMGYA00585244MGYA00585244completedamplicon[{'key': 'Submitted nucleotide sequences', 'value': '2285192'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after length filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '772970'}, {'key': 'Predicted SSU sequences', 'value': '772662'}, {'key': 'Predicted LSU sequences', 'value': '3'}]5.0False2021-07-23T09:24:06ILLUMINAIllumina MiSeqERS1871427samplesERR2098500runsMGYS00001935studies
3analysis-jobsMGYA00585245MGYA00585245completedamplicon[{'key': 'Submitted nucleotide sequences', 'value': '1863283'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1679669'}, {'key': 'Predicted SSU sequences', 'value': '1674242'}, {'key': 'Predicted LSU sequences', 'value': '0'}]5.0False2021-07-23T09:25:40ILLUMINAIllumina HiSeq 2500ERS1871411samplesERR2098439runsMGYS00001935studies
4analysis-jobsMGYA00585246MGYA00585246completedamplicon[{'key': 'Submitted nucleotide sequences', 'value': '1244024'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1110539'}, {'key': 'Predicted SSU sequences', 'value': '1107011'}, {'key': 'Predicted LSU sequences', 'value': '0'}]5.0False2021-07-23T09:27:22ILLUMINAIllumina HiSeq 2500ERS1871430samplesERR2098450runsMGYS00001935studies
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" - ], - "text/plain": [ - " type id attributes.accession \\\n", - "0 analysis-jobs MGYA00585242 MGYA00585242 \n", - "1 analysis-jobs MGYA00585243 MGYA00585243 \n", - "2 analysis-jobs MGYA00585244 MGYA00585244 \n", - "3 analysis-jobs MGYA00585245 MGYA00585245 \n", - "4 analysis-jobs MGYA00585246 MGYA00585246 \n", - "\n", - " attributes.analysis-status attributes.experiment-type \\\n", - "0 completed amplicon \n", - "1 completed amplicon \n", - "2 completed amplicon \n", - "3 completed amplicon \n", - "4 completed amplicon \n", - "\n", - " attributes.analysis-summary \\\n", - "0 [{'key': 'Submitted nucleotide sequences', 'value': '1474912'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after length filtering', 'value': '335618'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '335618'}, {'key': 'Predicted SSU sequences', 'value': '335119'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "1 [{'key': 'Submitted nucleotide sequences', 'value': '1178982'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1060738'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1060738'}, {'key': 'Predicted SSU sequences', 'value': '1049588'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "2 [{'key': 'Submitted nucleotide sequences', 'value': '2285192'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after length filtering', 'value': '772970'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '772970'}, {'key': 'Predicted SSU sequences', 'value': '772662'}, {'key': 'Predicted LSU sequences', 'value': '3'}] \n", - "3 [{'key': 'Submitted nucleotide sequences', 'value': '1863283'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1679669'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1679669'}, {'key': 'Predicted SSU sequences', 'value': '1674242'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "4 [{'key': 'Submitted nucleotide sequences', 'value': '1244024'}, {'key': 'Nucleotide sequences after format-specific filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after length filtering', 'value': '1110539'}, {'key': 'Nucleotide sequences after undetermined bases filtering', 'value': '1110539'}, {'key': 'Predicted SSU sequences', 'value': '1107011'}, {'key': 'Predicted LSU sequences', 'value': '0'}] \n", - "\n", - " attributes.pipeline-version attributes.is-private attributes.complete-time \\\n", - "0 5.0 False 2021-07-23T09:20:39 \n", - "1 5.0 False 2021-07-23T09:22:00 \n", - "2 5.0 False 2021-07-23T09:24:06 \n", - "3 5.0 False 2021-07-23T09:25:40 \n", - "4 5.0 False 2021-07-23T09:27:22 \n", - "\n", - " attributes.instrument-platform attributes.instrument-model \\\n", - "0 ILLUMINA Illumina HiSeq 2500 \n", - "1 ILLUMINA Illumina HiSeq 2500 \n", - "2 ILLUMINA Illumina MiSeq \n", - "3 ILLUMINA Illumina HiSeq 2500 \n", - "4 ILLUMINA Illumina HiSeq 2500 \n", - "\n", - " relationships.sample.data.id relationships.sample.data.type \\\n", - "0 ERS1871389 samples \n", - "1 ERS1871409 samples \n", - "2 ERS1871427 samples \n", - "3 ERS1871411 samples \n", - "4 ERS1871430 samples \n", - "\n", - " relationships.run.data.id relationships.run.data.type \\\n", - "0 ERR2098577 runs \n", - "1 ERR2098437 runs \n", - "2 ERR2098500 runs \n", - "3 ERR2098439 runs \n", - "4 ERR2098450 runs \n", - "\n", - " relationships.study.data.id relationships.study.data.type \n", - "0 MGYS00001935 studies \n", - "1 MGYS00001935 studies \n", - "2 MGYS00001935 studies \n", - "3 MGYS00001935 studies \n", - "4 MGYS00001935 studies " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "analyses.head()" ] @@ -523,23 +300,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "fd8ef42e-ea80-4a55-863e-12342dddab5a", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "analyses.groupby('attributes.instrument-model').size().plot(kind='pie', autopct='%1.1f%%')\n", "plt.title('Percentages of analysed samples by instrument type');" @@ -587,328 +353,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "0fee9320-4354-47ce-afbc-e44b2ef09c50", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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typeidattributes.accessionattributes.analysis-statusattributes.experiment-typeattributes.pipeline-versionattributes.is-privateattributes.complete-timeattributes.instrument-platformattributes.instrument-modelrelationships.sample.data.idrelationships.sample.data.typerelationships.run.data.idrelationships.run.data.typerelationships.study.data.idrelationships.study.data.typeSubmitted nucleotide sequencesNucleotide sequences after format-specific filteringNucleotide sequences after length filteringNucleotide sequences after undetermined bases filteringPredicted SSU sequencesPredicted LSU sequencesReads with predicted CDSReads with predicted RNAReads with InterProScan matchPredicted CDSPredicted CDS with InterProScan matchTotal InterProScan matches
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12analysis-jobsMGYA00585254MGYA00585254completedamplicon5.0False2021-07-23T09:39:02ILLUMINAIllumina HiSeq 2500ERS1871398samplesERR2098533runsMGYS00001935studies9949537570227570227570227553793N/AN/AN/AN/AN/AN/A
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21analysis-jobsMGYA00585263MGYA00585263completedamplicon5.0False2021-07-23T14:22:58ILLUMINAIllumina HiSeq 2500ERS1871374samplesERR2098570runsMGYS00001935studies11105775905055905055905055892301N/AN/AN/AN/AN/AN/A
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243analysis-jobsMGYA00608340MGYA00608340completedmetagenomic5.0False2022-09-20T11:01:47ILLUMINAIllumina HiSeq 4000ERS1871393samplesERR2098379runsMGYS00001935studies622485682572296425722964257229642150939264250407538338812776564257300261282230322748978
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108 rows × 28 columns

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"246 runs MGYS00001935 \n", - "247 runs MGYS00001935 \n", - "\n", - " relationships.study.data.type Submitted nucleotide sequences \\\n", - "2 studies 2285192 \n", - "12 studies 994953 \n", - "13 studies 670174 \n", - "17 studies 1323302 \n", - "21 studies 1110577 \n", - ".. ... ... \n", - "243 studies 62248568 \n", - "244 studies 64768750 \n", - "245 studies 51217703 \n", - "246 studies 49633529 \n", - "247 studies 62556828 \n", - "\n", - " Nucleotide sequences after format-specific filtering \\\n", - "2 772970 \n", - "12 757022 \n", - "13 478699 \n", - "17 765789 \n", - "21 590505 \n", - ".. ... \n", - "243 25722964 \n", - "244 30469803 \n", - "245 24766186 \n", - "246 23913778 \n", - "247 32656692 \n", - "\n", - " Nucleotide sequences after length filtering \\\n", - "2 772970 \n", - "12 757022 \n", - "13 478699 \n", - "17 765789 \n", - "21 590505 \n", - ".. ... \n", - "243 25722964 \n", - "244 30469803 \n", - "245 24766186 \n", - "246 23913778 \n", - "247 32656692 \n", - "\n", - " Nucleotide sequences after undetermined bases filtering \\\n", - "2 772970 \n", - "12 757022 \n", - "13 478699 \n", - "17 765789 \n", - "21 590505 \n", - ".. ... \n", - "243 25722964 \n", - "244 30469803 \n", - "245 24766186 \n", - "246 23913778 \n", - "247 32656692 \n", - "\n", - " Predicted SSU sequences Predicted LSU sequences Reads with predicted CDS \\\n", - "2 772662 3 N/A \n", - "12 755379 3 N/A \n", - "13 472895 5 N/A \n", - "17 764645 5 N/A \n", - "21 589230 1 N/A \n", - ".. ... ... ... \n", - "243 21509 39264 25040753 \n", - "244 18363 31760 28024481 \n", - "245 21370 38041 23518432 \n", - "246 34631 52285 19997664 \n", - "247 30276 53113 31171698 \n", - "\n", - " Reads with predicted RNA Reads with InterProScan match Predicted CDS \\\n", - "2 N/A N/A N/A \n", - "12 N/A N/A N/A \n", - "13 N/A N/A N/A \n", - "17 N/A N/A N/A \n", - "21 N/A N/A N/A \n", - ".. ... ... ... \n", - "243 83388 12776564 25730026 \n", - "244 62320 6244999 28646835 \n", - "245 76618 9443549 24051747 \n", - "246 98831 2645285 20608046 \n", - "247 107374 13136590 31875576 \n", - "\n", - " Predicted CDS with InterProScan match Total InterProScan matches \n", - "2 N/A N/A \n", - "12 N/A N/A \n", - "13 N/A N/A \n", - "17 N/A N/A \n", - "21 N/A N/A \n", - ".. ... ... \n", - "243 12822303 22748978 \n", - "244 6258524 10588518 \n", - "245 9468683 16474824 \n", - "246 2649968 4440764 \n", - "247 13171653 22950127 \n", - "\n", - "[108 rows x 28 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new dataframe called filtered_analyses which will include all lines from the\n", "# transformed_analyses dataframe except for the ones where the value in the \"Predicted LSU sequences\"\n", @@ -1583,23 +444,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "6e0210f7-d788-48a7-b327-2763b8183bbb", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "108" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "filtered_analyses.shape[0]" ] @@ -1634,35 +484,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "269794af-c4c8-49c3-a832-8c0ce8a5241b", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Files available for analysis MGYA00585242:\n", - "ERR2098577_MERGED_FASTQ.fasta.gz: Processed nucleotide reads\n", - "ERR2098577_MERGED_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", - "ERR2098577_MERGED_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", - "ERR2098577_MERGED_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR2098577_MERGED_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR2098577_MERGED_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "\n", - "Files available for analysis MGYA00585243:\n", - "ERR2098437_MERGED_FASTQ.fasta.gz: Processed nucleotide reads\n", - "ERR2098437_MERGED_FASTQ_SSU.fasta.gz: Reads encoding SSU rRNA\n", - "ERR2098437_MERGED_FASTQ_SSU_MAPSeq.mseq.gz: MAPseq SSU assignments\n", - "ERR2098437_MERGED_FASTQ_SSU_OTU.tsv: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR2098437_MERGED_FASTQ_SSU_OTU_TABLE_HDF5.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n", - "ERR2098437_MERGED_FASTQ_SSU_OTU_TABLE_JSON.biom: OTUs, counts and taxonomic assignments for SSU rRNA\n" - ] - } - ], + "outputs": [], "source": [ "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as session:\n", " for analysisId in analyses.head(2)['attributes.accession']:\n", @@ -2123,7 +950,7 @@ "novel_prevotella_df = resources_df[resources_df['attributes.taxon-lineage'] == 'd__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__']\n", "# Plot the catalogues\n", "novel_prevotella_df.groupby('relationships.catalogue.data.id').size().plot(kind='pie', autopct='%1.0f%%')\n", - "plt.title('Catalogues where the novel Prevotella species are found');" + "plt.title('Catalogues where the novel Prevotella species are found');\n" ] }, { From cde8397d5a9e9af6796b9ca7a80370a76f550d6b Mon Sep 17 00:00:00 2001 From: Tatiana Gurbich Date: Thu, 3 Aug 2023 22:07:57 +0100 Subject: [PATCH 4/4] Implemented suggestions from review --- .../GSC23 MGnify Workshop.ipynb | 88 ++++++++++--------- 1 file changed, 45 insertions(+), 43 deletions(-) diff --git a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb index 472dd12..1f0fed2 100644 --- a/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb +++ b/src/notebooks/Python Examples/GSC23 MGnify Workshop.ipynb @@ -123,7 +123,7 @@ "source": [ "#### Task 1: Browse the recently analysed studies and save them to a file\n", "\n", - "Let's start by loading 50 most recently analysed studies and saving them to a file called ```studies.csv``` (it would take too long to load all studies for this example so we limit the number at 50):" + "Let's start by loading the 50 most recently analysed studies and saving them to a file called ```studies.csv``` (it would take too long to load all studies for this example so we limit the number at 50):" ] }, { @@ -354,49 +354,49 @@ { "cell_type": "code", "execution_count": null, - "id": "0fee9320-4354-47ce-afbc-e44b2ef09c50", + "id": "cda3bcef-00fa-4473-a3c1-2751c47e160b", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Save all possible JSON keys into a list \"all_keys\"\n", - "all_keys = [\n", - " 'Submitted nucleotide sequences',\n", - " 'Nucleotide sequences after format-specific filtering',\n", - " 'Nucleotide sequences after length filtering',\n", - " 'Nucleotide sequences after undetermined bases filtering',\n", - " 'Predicted SSU sequences',\n", - " 'Predicted LSU sequences',\n", - " 'Reads with predicted CDS',\n", - " 'Reads with predicted RNA',\n", - " 'Reads with InterProScan match',\n", - " 'Predicted CDS',\n", - " 'Predicted CDS with InterProScan match',\n", - " 'Total InterProScan matches'\n", - "]\n", + "# First we define a function which we will call \"analysis_summary_to_df\" that will \n", + "# take the attributes.analysis-summary column, convert it into a dataframe and then \n", + "# return it as a Pandas pivot table, which is a table that summarises the dataframe\n", + "def analysis_summary_to_df(analysis):\n", + " summary_df = pd.json_normalize(analysis['attributes.analysis-summary'])\n", + " if summary_df.size:\n", + " return summary_df.pivot_table(index=None, columns=\"key\", values=\"value\", aggfunc=\"first\")\n", + " return summary_df\n", "\n", - "# Iterate over each row of the 'attributes.analysis-summary' column, extract information \n", - "# and save to a temporary dictionary. If an analysis doesn't have a value for any of the keys\n", - "# from the all_keys list, insert \"N/A\"\n", - "extracted_data = dict()\n", - "for row in analyses['attributes.analysis-summary']:\n", - " result_dict = {item['key']: item['value'] for item in row}\n", - " for key in all_keys:\n", - " add_value = result_dict[key] if key in result_dict else \"N/A\"\n", - " extracted_data.setdefault(key, list()).append(add_value)\n", - " \n", - "# Convert the dictionary to a DataFrame\n", - "extracted_df = pd.DataFrame(extracted_data)\n", "\n", - "# Concatenate the two dataframes into a new one called transformed_analyses \n", - "transformed_analyses = pd.concat([analyses, extracted_df], axis=1)\n", + "# Add a column \"summary_df\" to the dataframe \"analyses\". The column is generated using\n", + "# the function \"analysis_summary_to_df\" that we defined above and will hold the data\n", + "# from the JSONs that we are converting here.\n", + "analyses['summary_df'] = analyses.apply(analysis_summary_to_df, axis=1)\n", + "\n", + "# Now we will generate a new dataframe called analyses_summary_df\n", + "analyses_summary_df = None\n", + "\n", + "# Iterate through the rows of the analyses dataframe\n", + "for idx, row in analyses.iterrows():\n", + " # Set the index of the summary_df dataframe to the index of the line we are iterating through\n", + " row.summary_df.index = [idx]\n", + " # Add lines from summary_df to analyses_summary_df\n", + " if analyses_summary_df is None:\n", + " analyses_summary_df = row.summary_df\n", + " else:\n", + " analyses_summary_df = pd.concat([analyses_summary_df, row.summary_df])\n", "\n", - "# Remove the attributes.analysis-summary column from the new dataframe\n", - "transformed_analyses.drop('attributes.analysis-summary', axis=1, inplace=True)\n", + "# Concatenate the new analyses_summary_df dataframe (which has the data from JSON in column form)\n", + "# and our original analyses dataframe and save them to a new dataframe called transformed_analyses\n", + "transformed_analyses = pd.concat([analyses, analyses_summary_df], axis=1)\n", "\n", - "# Display the first few rows of the resulting dataframe\n", - "transformed_analyses.head()" + "# Remove the attributes.analysis-summary and summary_df columns, we no longer need them\n", + "transformed_analyses.drop(['attributes.analysis-summary', 'summary_df'], axis=1, inplace=True)\n", + "\n", + "# View the first few lines of the updated dataframe\n", + "transformed_analyses.head()\n" ] }, { @@ -420,11 +420,13 @@ "source": [ "# Create a new dataframe called filtered_analyses which will include all lines from the\n", "# transformed_analyses dataframe except for the ones where the value in the \"Predicted LSU sequences\"\n", - "# column is \"0\" or \"N/A\"\n", + "# column is \"0\" or no value (NaN)\n", "filtered_analyses = transformed_analyses[\n", - " ~(transformed_analyses['Predicted LSU sequences'].isin(['0', 'N/A']))\n", + " ~(transformed_analyses['Predicted LSU sequences'].isin(['0']))\n", "]\n", "\n", + "filtered_analyses = filtered_analyses.dropna(subset=[\"Predicted LSU sequences\"])\n", + "\n", "# print the filtered dataframe\n", "filtered_analyses" ] @@ -977,7 +979,7 @@ "id": "cbae69d4-594e-414d-be2a-8a5383d091a7", "metadata": {}, "source": [ - "For this exercise we will be working with pre-fetched data for all genomes. This is because fetching it takes a while given the number of genomes but if you were fetching it yourself, the code for that is below (expand by clicking on the three dots but please don't execute the code)." + "For this exercise we will be working with pre-fetched data for all genomes. This is because fetching it takes a while given the number of genomes but if you were fetching it yourself, the code for that is below (expand by clicking on the three dots but please don't uncomment and execute the code)." ] }, { @@ -992,11 +994,11 @@ }, "outputs": [], "source": [ - "endpoint = \"genomes\"\n", - "with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", - " resources_all = map(lambda r: r.json, mgnify.iterate(endpoint))\n", - "resources_all_df = pd.json_normalize(resources_all)\n", - "resources_all_df.to_parquet('all_genome_resources_Aug2023.parquet')" + "# endpoint = \"genomes\"\n", + "# with APISession(\"https://www.ebi.ac.uk/metagenomics/api/v1\") as mgnify:\n", + "# resources_all = map(lambda r: r.json, mgnify.iterate(endpoint))\n", + "# resources_all_df = pd.json_normalize(resources_all)\n", + "# resources_all_df.to_parquet('all_genome_resources_Aug2023.parquet')" ] }, {