`metamer`` is a workflow to cluster sequencing samples (paired raw fastq reads or assemblies) based on shared k-mers. It currently uses mash to compute distance between samples. It is useful for binning metagenomes or any sequencing samples into cluster based on their similarity.
PYTHON VERSION >= 3.6.6
numpy >= 1.15.1
scipy >= 1.3.0,
luigi >= 2.7.5,
plumbum >= 1.6.6,
pandas >= 0.23.4,
pathlib >= 1.0.1,
matplotlib >= 3.1.0,
Seaborn >= 0.9.0
mash >= 2.1.1
faqcs
MetaMer can be installed by following instructions below:
First install conda, if you dont have it installed. See instructions on how to install miniconda here. Install miniconda that supports Python 3.
After the installation, first create a conda environment and then install third party tools.
Third party tool dependencies can be installed using conda.
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda create --yes -n metamer_env python=3.6.6
conda create -n metamer_env
conda install --yes -c bioconda mash=2.1.1 -n metamer_env
conda install --yes -c bioconda faqcs -n metamer_env
source activate metamer_env
Then clone the metamer repository.
git clone https://github.com/LANL-Bioinformatics/metamer
Then change directory to metamer and install.
cd metamer
python setup.py install
If the installation was succesful, you should be able to type metamer -h and get a help message on how to use the tool.
metamer -h
And, you can run a quick test using
cd metamer
metamer -c luigi.cfg
metamer
metamer requires a luigi config file. An example config file can be found here.
Make a copy of luigi.cfg and edit it with information pertinent to your analysis.
After having a well annotated config file, metamer can be run by simply typing
/path/to/bin/metamer -c /path/to/luigi.cfg
Set what tool to use for generating MinHash distance in mash_tool. Right now, metamer only have one option and that is to use mash. Also, set the path to output folder in out_folder. All the outputs and intermediate files will be written in this folder.
[DEFAULT]
mash_tool = mash
# directory where all files are copied and kept
out_folder = tests/test_run
# folder with input data (paired raw reads and fasta genomes)
in_folder = tests/data/fqs
These are the parameters specific to luigi. Change log level to DEBUG if reporting an error.
[core]
log_level:ERROR
scheduler_url:http://localhost:8082/
Following parameters are running for Quality control using FaQCs. Please update the parameters if needed.
[RunAllQC]
# of CPUs to run the script
num_cpus = 2
# Trimmed read should have to be at least this minimum length
faqc_min_L = 50
# Trimmed read has greater than or equal to this number of continuous base "N" will be discarded.
n_cutoff = 10
Following parameters are specific for mash. Parameters for kmer and sketches could have the most effect.
[AllSketches]
in_folder = tests/data/fqs
# k-mer size
kmer = 21
# threads
threads = 2
# sketch size
sketch = 1000
seed = 439
# minimum occurence of k-mer to be included
min_copy = 1
Only need to set threads parameter for generating distance.
[Alldist]
# of threads to trigger
threads = 2
Set the parameter for threshold to call clusters. Maximum distance betwen two points to be part of a same cluster.
[ClusterSamples]
# maximum cophenetic distance between two points in a cluster
threshold = 0.3
For this step by step guide, we will use a small dataset that is packaged with this repo, so all the config file parameters are already set in the luigi.cfg file that is included in the repo.
metamer can process paired fastq raw reads and assembled fasta files from one folder. Paried raw reads must have names that end in *_R1.fastq and *_R2.fastq to identify them as part of one sample. These raw reads can also be compressed in gz format, and have names that end in *R1.fastq.gz and *R2.fastq.gz. Assembled fasta files must have *.fna or if compressed *.fna.gz. Do not forget to specify the full path to the input folder in the config file.
The provided test datasets in tests/data/fqs is as follows:
-rw-r--r--@ 1 usr st 1.2M Sep 3 14:36 GCA_000009205.2_ASM920v2_genomic.fna.gz
-rw-r--r-- 1 usr st 4.0M Sep 3 14:36 GCA_000085225.1_ASM8522v1_genomic.fna
-rw-r--r-- 1 usr st 1.1M Sep 3 14:36 GCA_003482325.1_ASM348232v1_genomic.fna.gz
-rw-r--r-- 1 usr st 7.9K Jun 10 16:33 SRR059450_h100_R1.fastq.gz
-rw-r--r-- 1 usr st 8.1K Jun 10 16:33 SRR059450_h100_R2.fastq.gz
-rw-r--r-- 1 usr st 7.4K Jun 10 16:33 SRR059451_R1.fastq.gz
-rw-r--r-- 1 usr st 7.4K Jun 10 16:33 SRR059451_R2.fastq.gz
-rw-r--r-- 1 usr st 1.9K Jun 10 16:33 SRR059453_R1.fastq.gz
-rw-r--r-- 1 usr st 1.8K Jun 10 16:33 SRR059453_R2.fastq.gz
-rw-r--r-- 1 usr st 9.7K Jun 10 16:33 SRR059454_R1.fastq.gz
-rw-r--r-- 1 usr st 9.7K Jun 10 16:33 SRR059454_R2.fastq.gz
-rw-r--r-- 1 usr st 9.6K Jun 10 16:33 SRR059455_R1.fastq.gz
-rw-r--r-- 1 usr st 9.5K Jun 10 16:33 SRR059455_R2.fastq.gz
-rw-r--r-- 1 usr st 9.2K Jun 10 16:33 SRR059456_R1.fastq.gz
-rw-r--r-- 1 usr st 9.3K Jun 10 16:33 SRR059456_R2.fastq.gz
-rw-r--r-- 1 usr st 9.0K Jun 10 16:33 SRR059457_R1.fastq.gz
-rw-r--r-- 1 usr st 9.1K Jun 10 16:33 SRR059457_R2.fastq.gz
-rw-r--r-- 1 usr st 7.3K Jun 10 16:33 SRR059458_R1.fastq.gz
-rw-r--r-- 1 usr st 7.3K Jun 10 16:33 SRR059458_R2.fastq.gz
-rw-r--r-- 1 usr st 7.6K Jun 10 16:33 SRR059459_R1.fastq.gz
-rw-r--r-- 1 usr st 7.6K Jun 10 16:33 SRR059459_R2.fastq.gz
metamer should be in the path if the user followed the instuction for installations outlined in the above sections. If thats the case, one can run metamer directly:
metamer -c luigi.cfg
For this tutorial, cd into the github repo
cd metamer
and run
metamer luigi.cfg
All the parameters in luigi.cfg has already been set for this run. Also,if metamer is not in the path, users can directly call the metamer from the bin folder using:
bin/metamer luigi.cfg
metamer outputs multiple files. The most important one being clusters.txt. It's a csv file that has name of the cluster in first column and sample names that belong to the cluster. An example clusters.txt file.
8,['SRR059455_.1.trimmed']
9,['SRR059456_.1.trimmed']
1,['GCA_000009205.2_ASM920v2_genomic.fna']
10,['SRR059451_.1.trimmed']
11,['SRR059457_.1.trimmed']
2,['GCA_000085225.1_ASM8522v1_genomic.fna']
12,['SRR059459_.1.trimmed']
3,['SRR059453_.1.trimmed']
4,['SRR059450_h100_.1.trimmed']
5,['GCA_003482325.1_ASM348232v1_genomic.fna']
6,['SRR059458_.1.trimmed']
7,['SRR059454_.1.trimmed']
metamer also outputs a distance matrix of mash distances (mash_dist.txt) in coordinate form and square form (dist.txt).