RNA-seq

Institut Curie - Nextflow rna-seq analysis pipeline

Introduction

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with conda / singularity containers making installation easier and results highly reproducible.

The first version of this pipeline was modified from the nf-core/rnaseq pipeline. See the nf-core project for more details.

Pipline summary

1. Trim adapters from sequencing reads (TrimGalore!
2. Separate host/graft reads for PDX model (xengsort)
3. Run quality control of sequencing reads (fastqc)
4. Align reads on ribosomal RNAs sequences when available (bowtie1)
5. Align reads on reference genome (STAR / hisat2)
6. Infer reads orientation (rseqc)
7. Dedicated quality controls
   • Saturation curves (preseq / R)
   • Duplicates (picard / dupRadar)
   • Reads annotation (qualimap / R)
   • Gene body coverage (qualimap)
8. Identito monitoring based on a list of known polymorphism (bcftools / R)
9. Generate counts table from aligned data or pseudo-alignment (STAR / featureCounts / HTSeqCounts/salmon)
10. Exploratory analysis (R)
11. Reference-guided de novo transcripts assembly (stringtie, scallop)
12. Present all QC results in a final report (MultiQC)

Quick help

```bash N E X T F L O W ~ version 20.10.0

Launching main.nf [awesome_archimedes] - revision: 7f7a25de60

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v4.1.0

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Usage:

The typical command for running the pipeline is as follows:

nextflow run main.nf --reads PATH --samplePlan PATH -profile STRING --genome STRING

MANDATORY ARGUMENTS: --genome STRING Name of the reference genome. --reads PATH Path to input data (must be surrounded with quotes) --samplePlan PATH Path to sample plan (csv format) with raw reads (if --reads is not specified) --aligner STRING [star, hisat2] Tool for reads alignment --counts STRING [star, featureCounts, HTseqCounts, salmon] Tool to use to estimate the raw counts per gene --pseudoAligner STRING [salmon] Tool for reads pseudo-alignment

INPUTS: --singleEnd For single-end input data --stranded STRING [auto, forward, reverse, no] Library strandness

PREPROCESSING: --pdx Deconvolute host/graft reads for PDX samples --trimming Trim adapters with TrimGalore

MAPPING: --bowtieOpts STRING Options for rRNA mapping with bowtie --hisat2Opts STRING Options for genome mapping with Hisat2 --saveAlignedIntermediates Save intermediates alignment files --starOpts STRING Options for STAR mapping --starTwoPass Run STAR in two pass mode

COUNTS: --featurecountsOpts STRING Options for featureCounts quantification --htseqOpts STRING Options for HTSeq quantification --salmonQuantOpts STRING Options for Salmon quantification

DE NOVO ASSEMBLY: --denovo STRING [stringtie, scallop] Tool for reference-guided assembly of RNA transcripts --scallopOpts STRING Options for Scallop analysis --stringtieOps STRING Options for Stringtie analysis

REFERENCES: --bed12 PATH Path to gene file (BED12) --fasta PATH Path to genome fasta file --fastaFai PATH Path to genome index fasta file --genomeAnnotationPath PATH Path to genome annotations folder --gtf PATH Path to GTF annotation file --hisat2Index PATH Path to Hisat2 indexes --bowtie2Index PATH Path to Bowtie2 indexes --polym PATH Path to BED file with polymorphisms for identito monitoring --rrna PATH Path to Bowtie indexes for rRNA mapping --salmonIndex PATH Path to Salmon indexes --starIndex PATH Path to STAR indexes --transcriptsFasta PATH Path to transcriptome fasta file

SKIP OPTIONS: --skipBigWig Disable bigwig generation with Deeptools --skipMarkDup Disable duplicates analysis with Picard/DupRadar --skipFastqc Disable Fastqc --skipGeneCountsAnalysis Disable exporatory analysis of genes count --skipIdentito Disable Identito --skipMultiqc Disable MultiQC --skipQC Disable quality controls on raw and aligned reads [fastqc, qualimap, preseq] --skipQualimap Disable RNA-seq quality controls with Qualimap --skipRrna Disable rRNA mapping --skipSaturation Disable saturation analysis with Preseq

OTHER OPTIONS: --metadata PATH Specify a custom metadata file for MultiQC --multiqcConfig PATH Specify a custom config file for MultiQC --name STRING Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic --outDir PATH The output directory where the results will be saved

======================================================= Available Profiles -profile test Run the test dataset -profile conda Build a new conda environment before running the pipeline. Use --condaCacheDir to define the conda cache path -profile multiconda Build a new conda environment per process before running the pipeline. Use --condaCacheDir to define the conda cache path -profile path Use the installation path defined for all tools. Use --globalPath to define the insallation path -profile multipath Use the installation paths defined for each tool. Use --globalPath to define the insallation path -profile docker Use the Docker images for each process -profile singularity Use the Singularity images for each process. Use --singularityImagePath to define the insallation path -profile cluster Run the workflow on the cluster, instead of locally

```

Quick run

The pipeline can be run on any infrastructure from a list of input files or from a sample plan as follow

Run the pipeline on a test dataset

See the conf/test.conf to set your test dataset.

nextflow run main.nf -profile test,conda

Run the pipeline from a sample plan

nextflow run main.nf --samplePlan MY_SAMPLE_PLAN --aligner 'star' --counts 'star' --genome 'hg38' --outDir MY_OUTPUT_DIR -profile conda

Run the pipeline on a computational cluster

echo "nextflow run main.nf --reads '*.R{1,2}.fastq.gz' --aligner 'star' --counts 'star' --genome 'hg19' --outDir MY_OUTPUT_DIR -profile singularity,cluster" | qsub -N rnaseq

Defining the '-profile'

By default (whithout any profile), Nextflow will excute the pipeline locally, expecting that all tools are available from your PATH variable.

In addition, we set up a few profiles that should allow you i/ to use containers instead of local installation, ii/ to run the pipeline on a cluster instead of on a local architecture. The description of each profile is available on the help message (see above).

Here are a few examples of how to set the profile option. See the full documentation for details.

```

Run the pipeline locally, using the paths defined in the configuration for each tool (see conf/path.config)

-profile path --globalPath INSTALLATION_PATH

Run the pipeline on the cluster, using the Singularity containers

-profile cluster,singularity --singularityImagePath SINGULARITYIMAGEPATH

Run the pipeline on the cluster, building a new conda environment

-profile cluster,conda --condaCacheDir CONDA_CACHE ```

Sample Plan

A sample plan is a csv file (comma separated) that list all samples with their biological IDs, with no header.

SAMPLEID,SAMPLENAME,PATHTOR1FASTQ,[PATHTOR2FASTQ]

Full Documentation

1. Installation
2. Reference genomes
3. Running the pipeline
4. Output and how to interpret the results
5. Troubleshooting

Credits

This pipeline has been written by the bioinformatics platform of the Institut Curie (P. La Rosa, N. Servant)

Citation

If you use this pipeline for your project, please cite it using the following doi: 10.5281/zenodo.7443721
Do not hesitate to use the Zenodo doi corresponding to the version you used !

Contacts

For any question, bug or suggestion, please use the issues system or contact the bioinformatics core facility.