CI
1ebb61f1e8
Build pipeline: vsh-ci-dev-jsbwk
Source commit: 1e1ffb315f
Source message: Merge pull request #17 from viash-hub/add_biobox_modules
- Migrate a number of components to biobox
- Fix tests
- Reduce size of test resources
- Prepare for Viash Hub
137 lines
4.8 KiB
Markdown
137 lines
4.8 KiB
Markdown
# RNAseq.vsh
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<!-- README.md is generated by running 'quarto render README.qmd' -->
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A version of the [nf-core/rnaseq](https://github.com/nf-core/rnaseq)
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pipeline (version 3.14.0) in the [Viash framework](http://www.viash.io).
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## Rationale
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We stick to the original nf-core pipeline as much as possible. This also
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means that we create a subworkflow for the 5 main stages of the pipeline
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as depicted in the [README](https://github.com/nf-core/rnaseq).
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## Getting started
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As test data, we can use the small dataset nf-core provided with [their
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`test`
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profile](https://github.com/nf-core/test-datasets/blob/rnaseq3/samplesheet/v3.10/samplesheet_test.csv):
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<https://github.com/nf-core/test-datasets/tree/rnaseq3/testdata/GSE110004>.
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A simple script has been provided to fetch those files from the github
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repository and store them under `testData/minimal_test` (the
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subdirectory is created to support `full_test` later as well):
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`bin/get_minimal_test_data.sh`.
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Additionally, a script has been provided to fetch some additional
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resources for unit testing the components. Thes will be stored under
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`testData/unit_test_resources`: `bin/get_unit test_data.sh`
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To get started, we need to:
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1. [Install
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`nextflow`](https://www.nextflow.io/docs/latest/getstarted.html)
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system-wide
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2. Fetch the test data:
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``` bash
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bin/minimal_test.sh
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bin/get_minimal_test_data.sh
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```
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## Running the pipeline
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To actually run the pipeline, we first need to build the components and
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pipeline:
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``` bash
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viash ns build --setup cb --parallel
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```
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Now we can run the pipeline using the command:
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``` bash
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nextflow run target/nextflow/workflows/pre_processing/main.nf \
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-profile docker \
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--id test \
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--input testData/minimal_test/SRR6357070_1.fastq.gz \
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--publish_dir testData/test_output/
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```
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Alternatively, we can run the pipeline with a sample sheet using the
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built-in `--param_list` functionality: (Read file paths must be
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specified relative to the sample sheet’s path)
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``` bash
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cat > testData/minimal_test/input_fastq/sample_sheet.csv << HERE
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id,fastq_1,fastq_2,strandedness
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WT_REP1,SRR6357070_1.fastq.gz;SRR6357071_1.fastq.gz,SRR6357070_2.fastq.gz;SRR6357071_2.fastq.gz,reverse
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WT_REP2,SRR6357072_1.fastq.gz,SRR6357072_2.fastq.gz,reverse
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RAP1_UNINDUCED_REP1,SRR6357073_1.fastq.gz,,reverse
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HERE
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nextflow run target/nextflow/workflows/rnaseq/main.nf \
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--param_list testData/minimal_test/input_fastq/sample_sheet.csv \
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--publish_dir "test_results/full_pipeline_test" \
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--fasta testData/minimal_test/reference/genome.fasta \
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--gtf testData/minimal_test/reference/genes.gtf.gz \
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--transcript_fasta testData/minimal_test/reference/transcriptome.fasta \
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-profile docker
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```
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## Pipeline sub-workflows and components
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The pipeline has 5 sub-workflows that can be run separately.
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1. Prepare genome: This is a workflow for preparing all the reference
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data required for downstream analysis, i.e., uncompress provided
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reference data or generate the required index files (for STAR,
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Salmon, Kallisto, RSEM, BBSplit).
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2. Pre-processing: This is a workflow for performing quality control on
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the input reads It performs FastQC, extracts UMIs, trims adapters,
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and removes ribosomal RNA reads. Adapters can be trimmed using
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either Trim galore! or fastp (work in progress).
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3. Genome alignment and quantification: This is a workflow for
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performing genome alignment using STAR and transcript quantification
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using Salmon or RSEM (using RSEM’s built-in support for STAR) (work
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in progress). Alignment sorting and indexing, as well as computation
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of statistics from the BAM files is performed using Samtools.
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UMI-based deduplication is also performed.
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4. Post-processing: This is a workflow for duplicate read marking
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(picard MarkDuplicates), transcript assembly and quantification
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(StringTie), and creation of bigWig coverage files.
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5. Pseudo alignment and quantification: This is a workflow for
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performing pseudo alignment and transcript quantification using
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Salmon or Kallisto.
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6. Final QC: This is a workflow for performing extensive quality
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control (RSeQC, dupRadar, Qualimap, Preseq, DESeq2, featureCounts).
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It presents QC for raw reads, alignments, gene biotype, sample
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similarity, and strand specificity (MultiQC).
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## Reusing components from biobox
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At the moment, this pipeline makes use of the following components from
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[biobox](https://github.com/viash-hub/biobox):
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- `gffread`
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- `star/star_genome_generate`
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- `star/star_align_reads`
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- `salmon/salmon_index`
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- `salmon/salmon_quant`
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- `featurecounts`
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- `samtools/samtools_sort`
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- `samtools/samtools_index`
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- `samtools/samtools_stats`
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- `samtools/samtools_flagstat`
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- `samtools/samtools_idxstats`
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- `multiqc` (work in progress - updating `assets/multiqc_config.yaml`)
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- `fastp` (work in progress)
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- `rsem/rsem_prepare_reference` (work in progress)
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- `rsem/rsem_calculate_expression` (work in progress)
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