Introduction

nf-core/rnaseqdge is a bioinformatics pipeline that analyses RNA sequencing data and performes differential gene expression analysis. It takes a samplesheet and FASTQ files as input, performs quality control, (pseudo-)alignment, produces a gene expression matrix, a QC report, and differentially expressed genes between two groups.

1. combine multiple RNA-seq runs per sample together
2. read quality control (FastQC)
3. summarize quality control of raw reads (MultiQC)
4. if reference fasta is not given, download latest human reference genome and transcriptome from Ensembl
5. multiple gene quantification routes:
   • aligner (slow):
     • STAR -> Salmon
     • STAR -> RSEM
   • pseudo-aligner (fast):
     • Salmon
     • Kallisto
6. differential gene expression analysis using DESeq2 and edgeR

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,fastq_1,fastq_2,group c1,Control1_1.fq.gz,Control1_2.fq.gz,control c2,Control2_1.fq.gz,Control2_2.fq.gz,control t1,Tumor1_1.fq.gz,Tumor1_2.fq.gz,tumor t2,Tumor2_1.fq.gz,Tumor2_2.fq.gz,tumor

Each row represents a pair of fastq files (currently only paired end supported).

Now, you can run the pipeline using:

bash nextflow run nf-core/rnaseqdge \ -profile docker \ --aligner <star_rsem|star_salmon|kallisto|salmon> \ --input samplesheet.csv \ --outdir <OUTDIR> or with custom genome:

bash nextflow run nf-core/rnaseqdge \ -profile docker \ --aligner <star_rsem|star_salmon|kallisto|salmon> \ --input samplesheet.csv \ --genome_fasta genome.fa.gz --genome_gtf genome_gtf.gtf --outdir <OUTDIR>

(Note: star_rsem and star_salmon index file creation can take multiple hours and is RAM intensive.)

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

• quality reports of fastq input files
• quantified gene expression table
• differentially expressed genes between two groups

Possible improvements:

• single-end reads
• include pre-made indeces of aligners to avoid construction
• more preprocessing: adapter trimming, removal ribosomal RNA, ...
• make salmon aligner decoy-aware
• add test run option
• add ext.args to config
• add covariables to DGE analysis
• add 3 or more group comparisons for DGE
• add singularity, conda, etc ..
• proper software versions output

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.