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This page provides a guide to QUT users on how to install and run the nextflow nf-core/rnaseq workflow on the HPC.

Pre-requisites

• Installed conda3 or miniconda3 ( https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html )

• Basic Unix command line knowledge (example: https://researchcomputing.princeton.edu/education/external-online-resources/linux ; https://swcarpentry.github.io/shell-novice/ )

  • https://sandbox.bio/

• Familiarity with one unix text editors (example Vi/Vim or Nano):

  • VIM ( https://bioinformatics.uconn.edu/vim-guide/ ; https://missing.csail.mit.edu/2020/editors/)

  • Nano (https://engineering.purdue.edu/ECN/Support/KB/Docs/BasictutorialforNanou ; https://www.howtogeek.com/howto/42980/the-beginners-guide-to-nano-the-linux-command-line-text-editor/ )

• Have an HPC account on QUT’s HPC compute. Apply for a new HPC account here.

• R tutorials:

  • https://girke.bioinformatics.ucr.edu/GEN242/tutorials/

Install Nextflow

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.

Additional information is available here: https://nf-co.re/usage/installation

Additional details on the workflow can be found at:

Overview: https://nf-co.re/rnaseq/3.0

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

Pipeline Summary

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• Preprocessing

  • FastQC - Raw read QC

  • UMI-tools extract - UMI barcode extraction

  • TrimGalore - Adapter and quality trimming

  • 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 - 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 similarity

• 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

Getting Started

Download and run the workflow using minimal data provided by nf-core/rnaseq. We recommend using singularity as the profile for QUT’s HPC. Another profile option can be ‘conda.’ Note: the profile option ‘docker’ is unavailable on the HPC.

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:

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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 1 \
        --three_prime_clip_r2 1 \
      -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:

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

Preparing to run on the HPC

To run this on the HPC a PBS submission script needs to be created using a text editor. For example, create a file called launch.pbs using a text editor of choice (i.e., vi or nano) and then copy and paste the code below:

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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 --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 1 \
        --three_prime_clip_r2 1

Submitting the job

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

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qsub launch.pbs

Monitoring the Run

You can use the command

qstat -u $USER

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