quick-rnaseq-nf

A basic and quick workflow for differential expression analysis.

Overview

Configuration

• codename: mnemonic codename for the run (default: 'quick-rnaseq')
• outdir: directory where to store the results (default: './results')
• experiment.samplesheet: CSV file describing samples and conditions (required).
• experiment.contrasts: contrasts for differential expression analysis in the format [['case1', 'control'],['case2','control']], which performs the analysis of case1 vs control samples, and case2 vs control samples. (required)
• transcriptome.reference: transcriptome reference file. Gencode recommended (required. GZ format)
• transcriptome.decoys: reference genome file. Gencode recommended (required. GZ format)
• fastp.args: options for reads trimming using fastp. (default: '')
• salmon.index.args: options for Salmon index, e.g. '--gencode' for Gencode transcritomes.
• salmon.quant.libtype: library type for quantification (default: 'A', Salmon infers lib type)
• salmon.quant.args: Salmon options. (default: '--validateMappings --gcBias')
• summarize_to_gene.counts_from_abundance: infer counts from abundances using tximeta (default: 'no')
• summarize_to_gene.organism_db: Bioconductor organism package for annotation. Currently supporting org.Hs.eg.db for Human and org.Mm.eg.db for mouse and (default: 'org.Hs.eg.db')
• qc.pca.transform: counts transformation for PCA analysis (see DESeq2. default: 'rlog')
• qc.ma.lfc_threshold: log fold-change threshold for MA plot (see DESeq2. default: 0)
• qc.sample.transform: counts transformation for PCA analysis (see DESeq2. default: 'rlog')
• dge.lfc_threshold: log fold-change threshold for differential expression analysis (see DESeq2. default: 0)
• dge.fdr: false discovery rate threshold to be used with implicit filtering (see DESeq2. default: 0.05)
• gene_ontology.organism_db: Bioconductor organism package for annotation. Currently supporting org.Hs.eg.db for Human and org.Mm.eg.db for mouse and (default: 'org.Hs.eg.db')
• gene_ontology.gene_id: type of gene id used for the analysis (default: 'ensembl')
• gene_ontology.remove_gencode_version: remove Gencode version from gene id (default: 'yes')
• gene_ontology.fdr: false discovery rate threshold (default: 0.05)

Running the workflow

Install or update the workflow

nextflow pull stracquadaniolab/quick-rnaseq-nf

Run the analysis with test data and Docker

nextflow run stracquadaniolab/quick-rnaseq-nf -profile test,docker

Run the analysis with test data and Singularity

nextflow run stracquadaniolab/quick-rnaseq-nf -profile test,singularity

Run the analysis with test data, Singularity and Slurm

nextflow run stracquadaniolab/quick-rnaseq-nf -profile test,singularity,slurm

Run the analysis on human data with Singularity and Slurm

Prepare a samplesheet.csv file as follows:

sample,read1,read2,condition
6C_REP1,data/RF01_6C1_R1_001.fastq.gz,data/RF01_6C1_R2_001.fastq.gz,control
6C_REP2,data/RF01_6C2_R1_001.fastq.gz,data/RF01_6C2_R2_001.fastq.gz,case

Please note that the header is required and it is case sensitive.

Prepare a nextflow.config file as follows:

params {
  // experiment information
  experiment.samplesheet = "./samplesheet.csv"
  experiment.contrasts = [['case1', 'control'],['case2','control']]

  // transcriptome information 
  transcriptome.reference = "gencode.v40.transcripts.fa.gz"
  transcriptome.decoys = "GRCh38.primary_assembly.genome.fa.gz"
}

Now you can run quick-rnaseq as follows:

nextflow run stracquadaniolab/quick-rnaseq-nf -profile singularity,slurm

Results

• results/analysis/dge-<contrasts>.csv: file with the differential expression analysis results for a given contrast.
• results/analysis/go-<contrasts>.csv: file with the GO analysis results for a given contrast.
• results/dataset/summarized-experiment.rds: DESeqDataset object with all experimental information (e.g. gene counts)
• results/qc: quality control report
• results/quantification/<sample-name>: Salmon quantification folders for each sample.

Authors

• Giovanni Stracquadanio, [email protected]