nf-core/rnaseq: Gene Expression Analysis (version 3.10.1)

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

Basic Unix command line knowledge (example: https://researchcomputing.princeton.edu/education/external-online-resources/linux ; The Unix Shell: Summary and Setup )
- sandbox.bio
Familiarity with one unix text editors (example Vi/Vim or Nano):
- VIM ( VIM Guide | Computational Biology Core ; Editors (Vim))
- Nano (Basic tutorial for Nano users ; 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:
- 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/3.10.1

Usage: https://nf-co.re/rnaseq/3.10.1/usage

GitHub: GitHub - nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

Pipeline Summary

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
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 similiarity
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

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 auto \
    --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,auto
control_2,/path/to/directory/containing/fastq_files/control-2_R1.fastq.gz,/path/to/directory/containing/fastq_files/control-2_R2.fastq.gz,auto
control_3,/path/to/directory/containing/fastq_files/control-3_R1.fastq.gz,/path/to/directory/containing/fastq_files/control-3_R2.fastq.gz,auto
infected_1,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-1_R2.fastq.gz,auto
infected_2,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-2_R2.fastq.gz,auto
infected_3,/path/to/directory/containing/fastq_files/infected-1_R1.fastq.gz,/path/to/directory/containing/fastq_files/infected-3_R2.fastq.gz,auto

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:

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.

Submitting the job

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

Submit the run with this command (On Lyra)

Monitoring the Run

You can use the command

Alternatively, use the following command:

To check on the jobs, you are running. Nextflow will launch additional jobs during the run.

You can also check the .nextflow.log file for details on what is going on.

Finally, if you have configured the connection to the NFTower, you can log on and check your run.

Troubleshooting

I have been using version 3.3. and now, when I run version 3.10.1, I get an error that the asset is corrupted. What should I do?

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:

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.