From 8068488f8f180e1cf2503882060c4799b76b55ba Mon Sep 17 00:00:00 2001 From: drosofff Date: Thu, 14 Sep 2023 12:31:21 +0200 Subject: [PATCH 1/2] Update mkdocs.yml --- mkdocs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mkdocs.yml b/mkdocs.yml index 2c391a461..727b8385e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -172,6 +172,8 @@ nav: - Differents visualizations of Seurat: scRNAseq_basics/visualization.md - References: scRNAseq_basics/references.md + - IOC on bulk RNAseq analysis: + - Introductive Session: bulk_RNAseq-IOC/IOC_RNAseq_01.md - Introduction to Deep-seq Analysis: - INTRODUCTION: deepseq/index.md - Illumina sequencing: deepseq/illumina.md From fb4b5835600e7d38f763aabc740a0d4a17faf93f Mon Sep 17 00:00:00 2001 From: drosofff Date: Thu, 14 Sep 2023 17:09:52 +0200 Subject: [PATCH 2/2] First drafting of RNAseq IOC (week1 to week 3) --- docs/bulk_RNAseq-IOC/01_IOC_RNAseq.md | 55 ++++ docs/bulk_RNAseq-IOC/Cutadapt.md | 64 ++++ docs/bulk_RNAseq-IOC/DEDESeq2.md | 36 +++ 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mode 100644 docs/bulk_RNAseq-IOC/mapping.md create mode 100644 docs/bulk_RNAseq-IOC/outline_conclusion.md create mode 100644 docs/bulk_RNAseq-IOC/read_filtering.md create mode 100644 docs/bulk_RNAseq-IOC/readcounts.md create mode 100644 docs/bulk_RNAseq-IOC/sequencing_strategies.md create mode 100644 docs/bulk_RNAseq-IOC/star.md create mode 100644 docs/bulk_RNAseq-IOC/strandness.md create mode 100644 docs/bulk_RNAseq-IOC/transcript_quant.md create mode 100644 docs/bulk_RNAseq-IOC/uploads.md create mode 100644 docs/bulk_RNAseq-IOC/visu_map.md create mode 100644 docs/bulk_RNAseq-IOC/volcano.md create mode 100644 docs/bulk_RNAseq-IOC/workflow_intro.md create mode 100644 docs/bulk_RNAseq-IOC/workflow_use.md diff --git a/docs/bulk_RNAseq-IOC/01_IOC_RNAseq.md b/docs/bulk_RNAseq-IOC/01_IOC_RNAseq.md new file mode 100644 index 000000000..ef04473ae --- /dev/null +++ b/docs/bulk_RNAseq-IOC/01_IOC_RNAseq.md @@ -0,0 +1,55 @@ +## Introduction to the IOC ARTbio 064: Bulk RNAseq Analyses +**November 2023** + +In this Interactive Online Companionship, we will train to perform RNAseq analyses +of Bulk RNAseq + +### Program / Schedule + +### Week 1 + + **3-hours Zoom video-conference with** + + 1. Introduction of the Companions and Instructors (10 min) + - Presentation of the IOC general workflow (Scheme) (15 min) + - Presentation of the IOC tools (2 hours) + 1. Zoom (5 min) + - Starbio (5 min) + - Slack (10 min) + - GitHub (20 min) + - Psilo storage (15 min) + - Galaxy (65 min) + +
    +
  1. Import data from Psilo to Galaxy +
  2. Program of the week 2 +
      +
    1. Presentation of exercises with digital tools +
    2. presentation of pretreatment and metadata organisations and of related tasks to be done +
    +
+ +### Week 2 +1. Question on Week 2 + 1. Data upload + 2. Quality control +- Program of Week 3 + 1. reference datasets (GTF, genome, subset, ucsc tables, ensembl Biomart) +### Week 3 +2. Questions on Week 2 + 1. reference + - GTF manipulation +- Program of the Week 3 + 1. Mapping and mappers + 2. Inspection of Bam files + +3. Analysis of the differential gene expression + 1. Count the number of reads per annotated gene + 2. Viewing datasets side by side using the Scratchbook + 3. Identification of the differentially expressed features + 4. Visualization of the differentially expressed genes + 5. Analysis of functional enrichment among the differentially expressed genes + +Some parts of this IOC were inspired by +[Reference-based RNAseq analysis](https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html) +of the Galaxy Training Network (GTN) diff --git a/docs/bulk_RNAseq-IOC/Cutadapt.md b/docs/bulk_RNAseq-IOC/Cutadapt.md new file mode 100644 index 000000000..8197ea8ed --- /dev/null +++ b/docs/bulk_RNAseq-IOC/Cutadapt.md @@ -0,0 +1,64 @@ +![](images/galaxylogo.png) + +# Filtering datasets to remove or trim low quality sequences + +## This step is optional and should be performed by 50% of attendees. + +## Cutadapt with single reads ![](images/tool_small.png) + +---- +1. Create a new history `Cutapdapt` (`wheel` --> `Create New`) ![](images/wheel.png) +2. Copy the fastq files from the RNAseq data library to this new history (`wheel` --> `Copy datasets`) +3. Select the `Cutadapt` tool +4. Start with selecting `Single-end` in the `Single-end or Paired-end reads?` menu +5. Select the multiple datasets button for this menu +6. Cmd-Click for discontinuous multiple selection of `single` fastq.gz files (3 datasets) +7. `Filter Options` + - `Minimum length`: 20 +8. `Read Modification Options` + - `Quality cutoff`: 20 +9. `Output Options` + - `Report`: Yes +10. Do not change the other available parameters and click `Execute` +---- + +## Cutadapt with paired-end reads ![](images/tool_small.png) + +---- +Repeat the same procedure as above, except that you select `Paired-end`in step 4: +Re-Run the tool using the re-run button on one Cutadapt instance and just select `Paired-end` +instead of `Single-end` + +- Then you have two input boxes, one for file #1 and one for file #2. + +- In the box `file #1` click the `multiple datasets` button and carefully Select +the fastq.gz files with the `_1` suffix + +- In the box `file #2` click the `multiple datasets` button and carefully Select +the fastq.gz files with the `_2` suffix + +- Do not change the other parameters (they are set to the same value as previously because +you used the re-run button). + +- Click the `Execute` button + +---- + +## Run MultiQC on Cutadapt jobs ![](images/tool_small.png) + +---- +1. Select `MultiQC` tool +2. Select `Cutadapt/Trim Galore!` in the menu `Which tool was used generate logs?` +3. Cmd-Select the `Report` datasets generated by Cutadapt +4. Press `Execute` +5. Now, the boring but essential job: Rename carefully the `Output` datasets generated +by Cutadapt. To do so, help yourself to the `Info` button at the bottom of dataset green +boxes. ![](images/info.png) + + Example: Rename `Cutadapt on data 10 and data 9: Read 2 Output` in `GSM461181_2_treat_paired.fastq.gz` + +6. Trash the 11 unfiltered/trimmed fastq.gz files. This is important to avoid mixing +filtered and non filtered datasets in the next steps. +---- + + diff --git a/docs/bulk_RNAseq-IOC/DEDESeq2.md b/docs/bulk_RNAseq-IOC/DEDESeq2.md new file mode 100644 index 000000000..0f3c52d73 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/DEDESeq2.md @@ -0,0 +1,36 @@ +![](images/galaxylogo.png) + +# `DESeq2` + +---- +![](images/tool_small.png) + + 1. Let's create a clean fresh history (`wheel` --> `Create New`) and name it DESeq2 ![](images/wheel.png) + 2. Copy the `.Counts`datasets from your `STAR`/ `HISAT2` history to this new history + (`wheel` --> `Copy datasets`) + 3. Select the `DESeq2` tool with the following parameters: + 1. `how`: Select group tags corresponding to levels + 2. In `Factor`: + 1. In `1: Factor` + - `Specify a factor name`: Treatment + - In `Factor level`: + - In `1: Factor level`: + - `Specify a factor level`: treated + - `Counts file(s)`: the 3 gene count files with `treat` in their name + - In `2: Factor level`: + - `Specify a factor level`: untreated + - `Counts file(s)`: the 4 gene count files with `untreat` in their name + 2. Click on `Insert Factor` (not on `Insert Factor level`) + 3. In `2: Factor` + - `Specify a factor name` to Sequencing + - In `Factor level`: + - In `1: Factor level`: + - `Specify a factor level`: Paired + - `Counts file(s)`: the 4 gene count files with `paired` in their name + - In `2: Factor level`: + - `Specify a factor level`: Single + - `Counts file(s)`: the 3 gene count files with `single` in their name + 3. `Files have header?`: Yes + 4. `Output normalized counts table`: Yes + 5. `Execute` + diff --git a/docs/bulk_RNAseq-IOC/DE_intro.md b/docs/bulk_RNAseq-IOC/DE_intro.md new file mode 100644 index 000000000..c943a2ee3 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/DE_intro.md @@ -0,0 +1,36 @@ +# Analysis of the differential gene expression using `DESeq2` + +![](images/lamp.png) + +---- + +DESeq2 is a great tool for Differential Gene Expression (DGE) analysis. +It takes read counts and combines them into a table (with genes in the rows and samples in the columns). +Importantly, it applies size factor normalization by: + +- Computing for each gene the geometric mean of read counts across all samples +- Dividing every gene count by the geometric mean accross samples +- Using the median of these ratios as a sample’s size factor for normalization + +Multiple factors with several levels can then be incorporated in the analysis. +After normalization we can compare the response of the expression of any gene to +the presence of different levels of a factor in a statistically reliable way. + +In our example, we have samples with two varying factors that can contribute to +differences in gene expression: + +- Treatment (either treated or untreated) +- Sequencing type (paired-end or single-end) + +Here, treatment is the primary factor that we are interested in. + +The sequencing type is further information we know about the data that might affect +the analysis. Multi-factor analysis allows us to assess the effect of the treatment, +while taking the sequencing type into account too. + +``` +We recommend that you add as many factors as you think may affect gene expression in +your experiment. It can be the sequencing type like here, but it can also be the +manipulation (if different persons are involved in the library preparation), +other batch effects, etc… +``` diff --git a/docs/bulk_RNAseq-IOC/DEseq2visu.md b/docs/bulk_RNAseq-IOC/DEseq2visu.md new file mode 100644 index 000000000..758d03a6c --- /dev/null +++ b/docs/bulk_RNAseq-IOC/DEseq2visu.md @@ -0,0 +1,117 @@ +![](images/galaxylogo.png) + +# Visualisation of differential expression + +Now we would like to extract the most differentially expressed genes due to the treatment, +and then visualize them using an heatmap of the normalized counts and also +the z-score for each sample. + +We will proceed in several steps: + +- Extract the most differentially expressed genes using the DESeq2 summary file +- Extract the normalized counts for these genes for each sample, using the normalized count file generated by DESeq2 +- Plot the heatmap of the normalized counts +- Compute the Z score of the normalized counts +- Plot the heatmap of the Z score of the genes + +## Extract the most differentially expressed genes + +---- +![](images/tool_small.png) + +1. Select the tool `Filter data on any column using simple expressions` to extract genes with a significant change in gene expression (adjusted p-value below 0.05) between treated and untreated samples: + 1. `Filter`: the DESeq2 result file + 2. `With following condition`: c7<0.05 + +The file with the independent filtered results can be used for further downstream analysis +as it excludes genes with only few read counts as these genes will not be considered as significantly differentially expressed. + +The generated file contains too many genes (632/STAR, ) to get a meaningful heatmap. Therefore, in the next step, +we will take only the genes with an absolute fold change > 2 (log2(fold change) > 1) + +---- +![](images/tool_small.png) + +1. Select the tool `Filter data on any column using simple expressions` + 1. `Filter`: the differentially expressed genes (output of previous `Filter` tool) + 2. `With following condition`: abs(c3)>1 + +We now have a table with 84/STAR, /HISAT2 lines corresponding to the most differentially expressed genes. +And for each of the gene, we have its id, its mean normalized counts (averaged over all +samples from both conditions), its log2FC and other information. + +We could plot the log2FC for the different genes, but here we would like to look at a +heatmap of expression for these genes in the different samples. So we need to extract the +normalized counts for these genes. + +We will join the normalized count table generated by DESeq2 with the table we just generated, +to conserve only the lines corresponding to the most differentially expressed genes. + +## Extract the normalized counts of the most differentially expressed genes + +---- +![](images/tool_small.png) + +- Create a Pasted Entry from the header line of the Filter output: + + 1. Copy the header of the final Filter output + 2. Using the Upload tool select Paste/Fetch data and paste the copied data + 3. *Set the Type to tabular* and select Start to upload a new Pasted Entry + +---- +![](images/tool_small.png) + +- Concatenate datasets tool to add this header line to the Filter output: + 1. select the `Concatenate datasets tail-to-head` tool + 2. select the Pasted entry dataset + 3. `+ Insert Dataset` + 4. select the final `Filter output` + +This ensures that the table of most differentially expressed genes has a header line and can be used in the next step. + +---- +![](images/tool_small.png) + +- join the normalized count table generated by DESeq2 with the table we just generated, +to conserve only the lines corresponding to the most differentially expressed genes + + 1. select the `Join two Datasets side by side on a specified field` tool + - `Join`: the Normalized counts file (output of DESeq2 tool) + - `using column`: Column: 1 + - `with`: most differentially expressed genes (output of the Concatenate tool tool) + - `and column`: Column: 1 + - `Keep lines of first input that do not join with second input`: No + - `Keep the header lines`: Yes + +The generated file has more columns than we need for the heatmap. In addition to the columns +with mean normalized counts, there is the log2FC and other information. +We need to remove the extra columns. + +---- +![](images/tool_small.png) + +- Cut tool to extract the columns with the gene ids and normalized counts: + + 1. Select the `Cut columns from a table`tool + - `Cut columns`: c1-c8 + - `Delimited by`: Tab + - `From`: the joined dataset (output of Join two Datasets tool) + +We now have a table with 85 lines (the most differentially expressed genes) +and the normalized counts for these genes in the 7 samples. + +---- +![](images/tool_small.png) + +- Plot the heatmap of the normalized counts of these genes for the samples + + 1. Select the `heatmap2` tool to plot the heatmap: + - `Input should have column headers`: the generated table (output of Cut tool) + - `Data transformation`: **Log2(value+1)** transform my data + - `Enable data clustering`: Yes + - `Labeling columns and rows`: Label columns and not rows + - `Coloring groups`: Blue to white to red + +You should obtain something similar to: + +![](images/cluster.png) diff --git a/docs/bulk_RNAseq-IOC/GO-intro.md b/docs/bulk_RNAseq-IOC/GO-intro.md new file mode 100644 index 000000000..932dff39a --- /dev/null +++ b/docs/bulk_RNAseq-IOC/GO-intro.md @@ -0,0 +1,17 @@ +![](images/lamp.png) + +# Analysis of functional enrichment among the differentially expressed genes + +We have extracted genes that are differentially expressed in treated (Pasilla gene-depleted) +samples compared to untreated samples. We would like to know if there are categories of +genes that are enriched among the differentially expressed genes. + +Gene Ontology (GO) analysis is widely used to reduce complexity and highlight biological +processes in genome-wide expression studies. + +However, standard methods give biased results on RNA-seq data due to over-detection +of differential expression for long and highly-expressed transcripts. + +The goseq tool provides methods for performing GO analysis of RNA-seq data, +taking length bias into account. The methods and software used by goseq are equally +applicable to other category based tests of RNA-seq data, such as KEGG pathway analysis. \ No newline at end of file diff --git a/docs/bulk_RNAseq-IOC/GO-tool.md b/docs/bulk_RNAseq-IOC/GO-tool.md new file mode 100644 index 000000000..ee11fe252 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/GO-tool.md @@ -0,0 +1,77 @@ +![](images/galaxylogo.png) + +## Prepare the datasets for GOSeq + +---- +![](images/tool_small.png) + +1. Select `Compute an expression on every row` tool with + - `Add expression`: bool(c7<0.05) + - `as a new column to`: the DESeq2 result file +---- +![](images/tool_small.png) + +1. `Cut` tool with + - `Cut columns`: c1,c8 + - `Delimited by`: Tab + - `From`: the output of the Compute tool +---- +![](images/tool_small.png) + +1. `Change Case` tool with + - `From`: the output of the previous Cut tool + - `Change case of columns`: c1 + - `Delimited by`: Tab + - `To`: Upper case + +This generates the first input for goseq. We need as second input for goseq, the gene lengths. +We can use there the gene length generated by featureCounts tool and reformat it a bit. + +---- +![](images/tool_small.png) + +1. Copy one output of type `...: Feature lengths` of the 7 featureCounts runs in the history `STAR`/`HISAT2` +2. Rename it `Lengths` +3. `Change Case` tool with + - `From`: the feature lengths (output of featureCounts tool) + - `Change case of columns`: c1 + - `Delimited by`: Tab + - `To`: Upper case + +We have now the two required input files for goseq. + +## Perform GO analysis + +---- +![](images/tool_small.png) + +1. Select `goseq` tool with + - `Differentially expressed genes file` : first file generated by Change Case tool on previous step + - `Gene lengths file` : second file generated by Change Case tool on previous step + - `Gene categories` : Get categories + - `Select a genome to use` : Fruit fly (dm6) + - `Select Gene ID format` : Ensembl Gene ID + - `Select one or more categories` : GO: Cellular Component, GO: Biological Process, GO: Molecular Function + +goseq generates a big table with the following columns for each GO term: + +|Column | Description | +|-----------------------------|-------------------------------------------------------------------------------------------------------| +|category | GO category | +|over_rep_pval |p-value for over representation of the term in the differentially expressed genes | +|under_rep_pval |p-value for under representation of the term in the differentially expressed genes | +|numDEInCat |number of differentially expressed genes in this category | +|numInCat |number of genes in this category | +|term |detail of the term | +|ontology |MF (Molecular Function - molecular activities of gene products), CC (Cellular Component - where gene products are active), BP (Biological Process - pathways and larger processes made up of the activities of multiple gene products)| +|p.adjust.over_represented |p-value for over representation of the term in the differentially expressed genes, adjusted for multiple testing with the Benjamini-Hochberg procedure | +|p.adjust.under_represented |p-value for over representation of the term in the differentially expressed genes, adjusted for multiple testing with the Benjamini-Hochberg procedure | + +To identify categories significantly enriched/unenriched below some p-value cutoff, it is necessary to use the adjusted p-value. + +#### How many GO terms are over-represented at adjusted P value < 0.05? +#### Under-represented? + +#### How are the over-represented GO terms divided between MF, CC and BP? +#### And for under-represented GO terms? + diff --git a/docs/bulk_RNAseq-IOC/QC.md b/docs/bulk_RNAseq-IOC/QC.md new file mode 100644 index 000000000..f32e6c4a4 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/QC.md @@ -0,0 +1,54 @@ +![](images/galaxylogo.png) + +# Quality Control + +## FastQC tool to analyse the fastq (or fastq.gz) datasets + +---- +![](images/tool_small.png) + + 1. Create a new history and name it `Quality Control` + + 2. Copy again all fastq.gz files from the data library into this history. You should + have 11 datasets in your history + + 3. Select the `fastqc` tool. + + 4. In the `Short read data from your current history` menu, select the `multiple datasets` button. ![](images/multiple-datasets.png) + + 5. Shift-Click to select all 11 datasets + + 6. Click `Execute` + ---- + + ![](images/oeil.png) + + - Look at the results of `FastQC`: These are the datasets named `FastQC on data xx: Webpage` + ---- + +## MultiQC to aggregate and have a general view of sequence qualities in the project + +---- + ![](images/tool_small.png) + + 1. Select the `MultiQC`tool (you can use the search bar). + + 2. `Which tool was used generate logs?` : Select `FastQC` + + 3. `Type of FastQC output?` : Select `Raw data` + + 4. `FastQC output` Cmd-Click (discontinuous, multiple selection) the *11* files named + `FastQC on xx: RawData` + + 5. Click `Execute` + ---- + + ![](images/oeil.png) + + Look at the result of `MultiQC`, dataset named `MultiQC on ...: Webpage` + + - Pay attention to the General Statistics that indicate the read sizes. + - Pay attention to the `Sequence Quality Histograms`. What can you say about the + quality of the samples ? + - Have a look to the `Adapter Content` section. + ---- diff --git a/docs/bulk_RNAseq-IOC/RNAseq_DE.md b/docs/bulk_RNAseq-IOC/RNAseq_DE.md new file mode 100644 index 000000000..9394cb269 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/RNAseq_DE.md @@ -0,0 +1,3 @@ +# ![](images/lamp.png) Statistical Analysis of Differential expression + +![](images/DE.png) \ No newline at end of file diff --git a/docs/bulk_RNAseq-IOC/bam.md b/docs/bulk_RNAseq-IOC/bam.md new file mode 100644 index 000000000..bd9da4b68 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/bam.md @@ -0,0 +1,31 @@ +# Inspection of BAM files + +---- +![](images/oeil.png) + +Click on the small eye icon of a Bam dataset (generated either with `RNA STAR` or `HISAT2`) +![](images/view-dataset.png) + +The header contains the chromosome specifications (their name and length) and other informations +such as the software that generation the Bam file and the command line to run the software. + +A BAM file (or a SAM file, the non compressed version) consists of: + +A header section (the lines starting with @) containing metadata, in particular the chromosome names and lengths (lines starting with the @SQ symbol) +An alignment section consisting of a table with 11 mandatory fields, as well as a variable number of optional fields: + +|Col | Field |Type | Brief Description | +|----|--------|-------|--------------------------------------| +|1 | QNAME |String | Query template NAME | +|2 | FLAG |Integer| bitwise FLAG | +|3 | RNAME |String |References sequence NAME | +|4 | POS |Integer|1-based leftmost mapping POSition | +|5 | MAPQ |Integer|MAPping Quality | +|6 | CIGAR |String |CIGAR String | +|7 | RNEXT |String |Ref. name of the mate/next read | +|8 | PNEXT |Integer|Position of the mate/next read | +|9 | TLEN |Integer|observed Template LENgth | +|10 | SEQ |String |segment SEQuence | +|11 | QUAL |String |ASCII of Phred-scaled base QUALity+33 | + +---- diff --git a/docs/bulk_RNAseq-IOC/cDNAs.md b/docs/bulk_RNAseq-IOC/cDNAs.md new file mode 100644 index 000000000..17f208cf1 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/cDNAs.md @@ -0,0 +1,9 @@ +# ![](images/lamp.png) cDNA synthesis + +## Oligo-dT + +![](images/oligodT.png) + +## Random priming + +![](images/randompriming.png) \ No newline at end of file diff --git a/docs/bulk_RNAseq-IOC/count.md b/docs/bulk_RNAseq-IOC/count.md new file mode 100644 index 000000000..011e2ee11 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/count.md @@ -0,0 +1,68 @@ +![](images/galaxylogo.png) + +# `featureCounts` + +---- +![](images/tool_small.png) + + 1. In your history `HISAT2` or `STAR` + 3. Select the `featureCounts` tool with the following parameters to count your reads: + 1. `Alignment file`: select multiple datasets button and shift-click the 7 bam files you have generated + 2. `Specify strand information`: **Unstranded** + 3. `Gene annotation file` : **in your history** + - `Gene annotation file`: **Drosophila_melanogaster.BDGP6.95.gtf** + 4. `FASTA/Q file`: **Gene-ID "\t" read-count (MultiQC/DESeq2/edgeR/limma-voom compatible)** + 5. `Create gene-length file`: Yes + 6. In `Options for paired-end reads`: + - `Count fragments instead of reads`: **Enabled; fragments (or templates) will be counted instead of reads** + 7. In `Advanced options`: + - `GFF feature type filter`: **exon** + - `GFF gene identifier`: gene_id + - `Allow read to contribute to multiple features`: No + - `Count multi-mapping reads/fragments`: Disabled; multi-mapping reads are excluded (default) + - `Minimum mapping quality per read`: 10 + 8. Leave other settings as defaults + 3. `Execute` + +---- +![](images/rename_datasets.png) + +You need now to rename you datasets to facilitate your downstream analysis. + +Be quiet and focus ! No hurry, this is an *important task* in the analysis. + +1. Search and select datasets with `featurecounts` (as we did before for renaming datasets) + +2. Click on the info icon ![](images/info.png) of `featureCounts on xxx: Counts` + +3. Copy the name of the dataset `Alignment file` in the tool parameters table (for instance, `GSM461176_untreat_single.bam`) +4. Now click on the pencil icon of the *_same_* dataset +5. Paste your text in the `Name` field of the dataset +6. Edit your text by replacing `bam` by `Counts` +(e.g. GSM461176_untreat_single.Counts) +8. repeat _ad lib_ for all counts files generated by featureCounts + +# `MultiQC` + +We have now generated (1) Bam alignments and (2) Counts files with feature counts, and +we have carefully and courageously edited the names of generated datasets. We are going +to be rewarded for this effort in the next steps ! + +---- +![](images/tool_small.png) + + 1. In your history `HISAT2` or `STAR` + 2. Select the `MultiQC` tool with the following parameters: + 1. `1: Results`/ `Which tool was used generate logs?`: **STAR** `or` **HISAT2** (depending on your analysis track) + 2. `STAR or HISAT output`: shift-click select all files with the extension **.log** + 3. Click on the `+ Insert Result` button + 4. `2: Results`/ `Which tool was used generate logs?`: **featureCounts** + 5. `Output of FeatureCounts`: shift-click select all files with the extension **: Summary** + 3. `Execute` + +---- +![](images/oeil.png) Examine the results and ![](images/lamp.png) + + + + diff --git a/docs/bulk_RNAseq-IOC/hisat2.md b/docs/bulk_RNAseq-IOC/hisat2.md new file mode 100644 index 000000000..c2141bf64 --- /dev/null +++ b/docs/bulk_RNAseq-IOC/hisat2.md @@ -0,0 +1,71 @@ +![](images/galaxylogo.png) + +# HISAT2 (option for 50 % of attendees) + +---- +![](images/tool_small.png) + + 1. create a new history and name it `HISAT2` + 2. Import the 11 datasets from the RNAseq data library to this `HISAT2` history, plus the Drosophila_melanogaster.BDGP6.95.gtf file + 3. Select the `HISAT2` tool with the following parameters to map your reads on the reference genome: + 1. `Source for the reference genome`: Use a builtin genome + 2. `Select a reference genome` : **dm6** + 3. `Is this a single or paired library:` Single-End + 4. `FASTA/Q file` : + - `GSM461176_untreat_single.fastq.gz` + - `GSM461179_treat_single.fastq.gz` + - `GSM461182_untreat_single.fastq.gz` + 5. `Specify strand information:` Unstranded + 6. `Summary options` + - `Output alignment summary in a more machine-friendly style.`: YES + - `Print alignment summary to a file.`: YES + 6. Leave other settings as defaults + 4. `Execute` +---- +![](images/redo.png) + + Redo the HISAT2 run for paired-end files + + 3. Rerun the `HISAT2` tool with the following parameters to map your reads on the reference genome: + 1. `Source for the reference genome`: Use a builtin genome + 2. `Select a reference genome` : **dm6** + 3. `Is this a single or paired library:` Paired-End + 4. `FASTA/Q file #1`: + - `GSM461177_1_untreat_paired.fastq.gz` + - `GSM461178_1_untreat_paired.fastq.gz` + - `GSM461180_1_treat_paired.fastq.gz` + - `GSM461181_1_treat_paired.fastq.gz` + 4. `FASTA/Q file #2`: + - `GSM461177_2_untreat_paired.fastq.gz` + - `GSM461178_2_untreat_paired.fastq.gz` + - `GSM461180_2_treat_paired.fastq.gz` + - `GSM461181_2_treat_paired.fastq.gz` + 5. `Specify strand information:` Unstranded + 6. Leave other settings as defaults (since you are redoing a run) + 4. `Execute` + +## Rename your datasets ! + +---- +![](images/rename_datasets.png) + +You need now to rename you datasets to facilitate your downstream analysis. + +Be quiet and focus ! No hurry, this is an *important task* in the analysis. + +1. Search and select datasets with HISAT2 ![](images/search_hisat2.png) + +2. Click on the info icon ![](images/info.png) of both `(BAM)` and `Mapping summary` files + +3. Copy the name or one of the two names of the datasets as shown bellow +![](images/copy.png) +4. Now click on the pencil icon of the *_same_* dataset +![](images/pencil.png) +5. Paste your text in the `Name` field of the dataset +![](images/paste.png) +6. Edit your text as follow for `Mapping summary` files +![](images/edit_log.png) +7. 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