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Prepared by the eResearch Office, QUT.

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The nf-core/rnaseq workflow requires Nextflow to be installed in your account on the HPC. Find details on how to install and test Nextflow here. Prepare a nextflow.config file and run a PBS pro submission script for Nextflow pipelines.

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GitHub: https://github.com/nf-core/rnaseq

Pipeline Summary

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The pipeline is built using Nextflow and processes data using the following steps:

• Preprocessing

  • cat - Merge re-sequenced FastQ files

  • FastQC - Raw read QC

  • UMI-tools extract - UMI barcode extraction

  • TrimGalore - Adapter and quality trimming

  • BBSplit - Removal of genome contaminants

  • SortMeRNA - Removal of ribosomal RNA (optional)

• Alignment and quantification

  • STAR and Salmon  - Fast spliced aware genome alignment and transcriptome quantification

  • STAR via RSEM   - Alignment and quantification of expression levels

  • HISAT2   - Memory efficient splice aware alignment to a reference

• Alignment post-processing

  • SAMtools SAMtools - Sort and index alignments

  • UMI-tools dedup  - UMI-based deduplication

  • picard MarkDuplicates  - Duplicate read marking

• Other steps

  • StringTie  - Transcript assembly and quantification

  • BEDTools and bedGraphToBigWig  - Create bigWig coverage files

• Quality control

  • RSeQC  - Various RNA-seq QC metrics

  • Qualimap  - Various RNA-seq QC metrics

  • dupRadar  - Assessment of technical / biological read duplication

  • Preseq  - Estimation of library complexity

  • featureCounts  - Read counting relative to gene biotype

  • DESeq2  - PCA plot and sample pairwise distance heatmap and dendrogram

  • MultiQC  - Present QC for raw reads, alignment, read counting , and sample similaritysimiliarity

• Pseudo-alignment and quantification

  • Salmon  - Wicked fast gene and isoform quantification relative to the transcriptome

• Workflow reporting and genomes

  • Reference genome files  - Saving reference genome indices/files

  • Pipeline information  - Report metrics generated during the workflow execution

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nextflow run nf-core/rnaseq -profile test,singularity --outdir results -r 3.10.1

Running the pipeline using custom data

Example of a typical command to run an RNA-seq analysis for mouse samples:

nextflow run nf-core/rnaseq --input samplesheet.csv \
        --outdir results \
        -r 3.10.1 \
        --genome GRCh38 \
        -profile singularity \
        --aligner star_rsem \
        --clip_r1 10 \
        --clip_r2 10 \
        --three_prime_clip_r1 2 \
        --three_prime_clip_r2 2

Note, if the running was interrupted or you did not complete a particular step, or you want to modify a parameter for a particular step, instead of re-running all processes again, nextflow enables you to “-resume” the workflow.

nextflow run nf-core/rnaseq --input samplesheet.csv \
        --outdir results \
        -r 3.10.1 \
        --genome GRCh38 \
        -profile singularity \
        --aligner star_rsem \
        --clip_r1 10 \
        --clip_r2 10 \
        --three_prime_clip_r1 2 \
        --three_prime_clip_r2 2 \
      -resume

Preparing a ‘samplesheet.csv’ file

Prepare a sample sheet file that specifies the input files to be used. To do this, we use an nf-core script to generate the ‘samplesheet.csv’ file as follows (setting strandedness to auto allows the pipeline to determine the strandedness of your RNA-seq data automatically):

#load python 3.10
module load python/3.10.8-gcccore-12.2.0

#download script and make executable
wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py
chmod +x fastq_dir_to_samplesheet.py

#generate the samplesheet.csv file
./fastq_dir_to_samplesheet.py /path/to/directory/containing/fastq_files/ samplesheet.csv \
    --strandedness unstrandedauto \
    --read1_extension _R1.fastq.gz \
    --read2_extension _R2.fastq.gz

Example index.csv (Version 3.10.1):

sample,fastq_1,fastq_2,strandedness
control_1,/path/to/directory/containing/fastq_files/control-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/control-1_R2.fastq.gz,unstrandedauto
control_2,/path/to/directory/containing/fastq_files/control-2_R1.fastq.gz,/path/to/directory/containing/fastq_files/control-2_R2.fastq.gz,unstrandedauto
control_3,/path/to/directory/containing/fastq_files/control-3_R1.fastq.gz,/path/to/directory/containing/fastq_files/control-3_R2.fastq.gz,unstrandedauto
infected_1,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-1_R2.fastq.gz,unstrandedauto
infected_2,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-2_R2.fastq.gz,unstrandedauto
infected_3,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-3_R2.fastq.gz,unstrandedauto

Preparing to run on the HPC

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#!/bin/bash -l
#PBS -N nfrna2
#PBS -l select=1:ncpus=2:mem=4gb
#PBS -l walltime=24:00:00

#work on current directory (folder)
cd $PBS_O_WORKDIR

#load java and set up memory settings to run nextflow
module load java
NXF_OPTS='-Xms1g -Xmx4g'

#run the rnaseq pipeline
nextflow run nf-core/rnaseq \
      -profile singularity \
      -r 3.10.1 \
      --input samplesheet.csv \
      --genome GRCm38 GRCh38 \
      --outdir results \
      --aligner star_salmon

We recommend running the nextflow nf-core/rnaseq pipeline once and then assessing the fastqc results folder to assess if sequence biases are present in the 5'-end and 3'-end ends of the sequences. Then, we can use the PBS script below to tell the pipeline to remove a defined number of bases from the 5'-end (--clip_r1 or --clip_r2) or 3'-end (--three_prime_clip_r1 or --three_prime_clip_r2). Also, we can specify to remove ribosomal RNA as these sets of sequences are non-informative.

#!/bin/bash -l
#PBS -N nfrna2
#PBS -l select=1:ncpus=2:mem=4gb
#PBS -l walltime=24:00:00

#work on current directory (folder)
cd $PBS_O_WORKDIR

#load java and set up memory settings to run nextflow
module load java
NXF_OPTS='-Xms1g -Xmx4g'

#run the rnaseq pipeline
nextflow run nf-core/rnaseq --input samplesheet.csv \
        --outdir results \
        -r 3.10.1 \
        --genome GRCh38 \
        -profile singularity \
        --aligner star_rsemsalmon \
        --clip_r1 10 \
        --clip_r2 10 \
        --three_prime_clip_r1 2 \
        --three_prime_clip_r2 2

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Once you have created the folder for the run, the inputsamplesheet.tsv csv file, nextflow.config, and launch.pbs, you are ready to submit.

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#delete the existing assests associated with the RNAseq pipeline:
cd ~/.nextflow/assets/nf-core
rm -r rnaseq/

#run again a test with the new version that you are testing, for example, version 3.10.1. See details on how to run a test above (under 'Getting Started')

Add output folders/files

sample data

Running the pipeline using custom data

Example of a typical command to run an RNA-seq analysis for mouse samples:

nextflow run nf-core/rnaseq --input samplesheet.csv \
        --outdir results \
        -r 3.10.1 \
        --genome GRCm38 \
        -profile singularity \
        --aligner star_rsem \
        --clip_r1 10 \
        --clip_r2 10 \
        --three_prime_clip_r1 2 \
        --three_prime_clip_r2 2

Note, if the running was interrupted or you did not complete a particular step, or you want to modify a parameter for a particular step, instead of re-running all processes again, nextflow enables you to “-resume” the workflow.

nextflow run nf-core/rnaseq --input samplesheet.csv \
        --outdir results \
        -r 3.10.1 \
        --genome GRCm38 \
        -profile singularity \
        --aligner star_rsem \
        --clip_r1 10 \
        --clip_r2 10 \
        --three_prime_clip_r1 2 \
        --three_prime_clip_r2 2 \
      -resume