Run the nextflow nf-core/rnaseq pipeline in the HPC cluster. Exercises include:
Running a test to verify the execution of the pipeline
Running QC check to determine read trimming parameters
Running the full nf-core/rnaseq pipeline.
Work in the HPC |
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Before we start using the HPC, let’s start an interactive session:
qsub -I -S /bin/bash -l walltime=10:00:00 -l select=1:ncpus=1:mem=4gb |
Get a copy of the scripts to be used in this module
Use the terminal to log into the HPC and create a /RNAseq/ folder to run the nf-core/rnaseq pipeline. For example:
mkdir -p $HOME/workshop/scripts cp /work/training/rnaseq/scripts/* $HOME/workshop/scripts/ ls -l $HOME/workshop/scripts/ |
Line 1: The -p indicates create 'parental directories as required. Thus the line 1 command creates both /workshop/ and the subfolder /workshop/scripts/
Line 2: Copies all files from /work/datasets/workshop/scripts/ as noted by an asterisk to the newly created folder $HOME/workshop/scripts/
Copy public data to your $HOME
mkdir -p $HOME/workshop/data cp /work/training/rnaseq/data/* $HOME/workshop/data/ # list the content of the $HOME/workshop/data/ |
Line 1: The first command creates the folder /scripts/
Line 2: Copies all files from /work/datasets/workshop/scripts/ folder as noted by an asterisk to newly created $HOME/workshop/scripts/ folder
Line 3: a quick challenge - see the previous section for hints
Create a folder for running the nf-RNA-seq pipeline
Let’s create an “RNAseq” folder to run the nf-core/rnaseq pipeline and move into it. For example:
mkdir -p $HOME/workshop/RNAseq mkdir $HOME/workshop/RNAseq/run1_test mkdir $HOME/workshop/RNAseq/run2_QC mkdir $HOME/workshop/RNAseq/run3_RNAseq cd $HOME/workshop/ |
Lines 1-4: create sub-folders for each exercise
Line 5: change the directory to the folder “run1_test”
Line 6: print the current working directory
First, let’s assess the execution of the nf-core/rnaseq pipeline by running a test using sample data.
Copy the launch_nf-core_RNAseq_test.pbs to the working directory
cd $HOME/workshop/RNAseq/run1_test cp $HOME/workshop/scripts/launch_nf-core_RNAseq_test.pbs . |
View the content of the script as follows:
cat launch_nf-core_RNAseq_test.pbs |
#!/bin/bash -l #PBS -N nfrnaseq_test #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=24:00:00 #work on current directory cd $PBS_O_WORKDIR #load java and set up memory settings to run nextflow module load java export NXF_OPTS='-Xms1g -Xmx4g' nextflow run nf-core/rnaseq -r 3.12.0 -profile test,singularity --outdir results |
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nextflow command: nextflow run
pipeline name: nf-core/rnaseq
pipeline version: -r 3.12.0
container type and sample data: -profile test,singularity
output directory: --outdir results
Submitting the job
Submit the test job to the HPC cluster as follows:
qsub launch_nf-core_RNAseq_test.pbs |
Monitoring the Run
qjobs |
The pipeline requires preparing at least 2 files:
Metadata file (samplesheet.csv) that specifies the name of the samples, location of FASTQ files ('Read 1' and ‘Read 2’), and strandedness (forward, reverse, or auto. Note: auto is used when the strandedness of the data is unknown)
PBS Pro script (launch_nf-core_RNAseq_QC.pbs) with instructions to run the pipeline
Change to the data folder directory:
cd $HOME/workshop/data/ pwd |
Copy the bash script to the working folder
cp $HOME/workshop/scripts/create_RNAseq_samplesheet.sh $HOME/workshop/data/ |
Note: you could replace ‘$HOME/workshop/data’ with “.” A dot indicates ‘current directory’ and will copy the file to the directory where you are currently located
View the content of the script:
cat create_samplesheet_nf-core_RNAseq.sh |
Example for Single-End data (only ‘Read 1’ is available):
#!/bin/bash -l #User defined variables ########################################################## DIR='$HOME/workshop/data/' INDEX='samplesheet.csv' ########################################################## #load python module module load python/3.10.8-gcccore-12.2.0 #fetch the script to create the sample metadata table wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py chmod +x fastq_dir_to_samplesheet.py #generate initial sample metadata file ./fastq_dir_to_samplesheet.py $DIR $INDEX \ --strandedness auto \ --read1_extension .fastq.gz |
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Let’s generate the metadata file by running the following command:
sh create_RNAseq_samplesheet.sh |
Check the newly created samplesheet.csv file:
ls -l cat samplesheet.cvs |
sample,fastq_1,fastq_2,strandedness CD49fmNGFRm_rep1,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622174_1.fastq.gz,,auto CD49fmNGFRm_rep2,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622175_1.fastq.gz,,auto CD49fmNGFRm_rep3,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622177_1.fastq.gz,,auto CD49fpNGFRp_rep1,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622178_1.fastq.gz,,auto CD49fpNGFRp_rep2,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622179_1.fastq.gz,,auto CD49fpNGFRp_rep3,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622180_1.fastq.gz,,auto MTEC_rep1,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622172_1.fastq.gz,,auto MTEC_rep2,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622173_1.fastq.gz,,auto MTEC_rep3,/work/eresearch_bio/training/data/rnaseq_data/mouse_PRJNA862107/SRR20622176_1.fastq.gz,,auto |
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Copy the PBS Pro script for QC (launch_nf-core_RNAseq_QC.pbs)
Copy and paste the code below to the terminal:
cp $HOME/workshop/data/samplesheet.csv $HOME/workshop/RNAseq/run2_QC cp $HOME/workshop/scripts/launch_nf-core_RNAseq_QC.pbs $HOME/workshop/RNAseq/run2_QC cd $HOME/workshop/RNAseq/run2_QC |
Line 1: Copy the samplesheet.csv file to the working directory
Line 2: move to the working directory
Line 3: copy the launch_nf-core_RNAseq_QC.pbs submission script to the working directory
View the content of the launch_nf-core_RNAseq_QC.pbs script:
cat launch_nf-core_RNAseq_QC.pbs |
#!/bin/bash -l #PBS -N nfrnaseq_QC #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=24:00:00 #work on current directory cd $PBS_O_WORKDIR #load java and set up memory settings to run nextflow module load java export NXF_OPTS='-Xms1g -Xmx4g' nextflow run nf-core/rnaseq \ -profile singularity \ -r 3.12.0 \ --input samplesheet.csv \ --outdir results \ --genome GRCm38-local \ --skip_trimming \ --skip_alignment \ --skip_pseudo_alignment |
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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.
Version 3.12.0 allows running the pipeline to do quality assessment only, without any alignment, read counting, or trimming.
Once you have created the folder for the run, the samplesheet.csv file, and launch.pbs, you are ready to submit the job to the HPC scheduler:
qsub launch_nf-core_RNAseq_QC.pbs |
qjobs |
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.
NOTE: To proceed, you need to be on QUT’s WiFi network or signed via VPN.
To browse the working folder in the HPC type in the file finder:
Windows PC
\\hpc-fs\work\training\rnaseq |
Mac
smb://hpc-fs/work/training/rnaseq |
Evaluate the nucleotide distributions in the 5'-end and 3'-end of the sequenced reads (Read1 and Read2). Look into the “MultiQC” folder and open the provided HTML report.
Items to check:
The overall quality of the experiment and reads. Look at the “Sequence Quality Histogram” plot. For example, if Phred assigns a quality score of 30 to a base, the chances that this base is called incorrectly are 1 in 1000. Phred quality scores are logarithmically linked to error probabilities.
Phred Quality Score | Probability of incorrect base call | Base call accuracy |
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10 | 1 in 10 | 90% |
20 | 1 in 100 | 99% |
30 | 1 in 1000 | 99.9% |
40 | 1 in 10,000 | 99.99% |
50 | 1 in 100,000 | 99.999% |
60 | 1 in 1,000,000 | 99.9999% |
Assess QC reports (FastQC and MultiQC) to define how many nucleotides should be trimmed from the 5'-end and/or 3-end regions of the FASTQ reads (see Case 3 below).
Copy and paste the code below to the terminal:
cp $HOME/workshop/data/samplesheet.csv $HOME/workshop/RNAseq/run3_RNAseq cd $HOME/workshop/RNAseq/run3_RNAseq pwd |
Line 1: Copy the samplesheet.csv file to the working directory
Line 2: move to the working directory
Line 3: print working directory → verify the folder location
Copy the PBS Pro script to run the nf-core/rnaseq pipeline:
cp $HOME/workshop/scripts/launch_nf-core_RNAseq_pipeline.pbs $HOME/workshop/RNAseq/run3_RNAseq |
Print the content of the launch_RNAseq.pbs script:
cat launch_nf-core_RNAseq_pipeline.pbs |
#!/bin/bash -l #PBS -N nfRNAseq #PBS -l select=1:ncpus=2:mem=4gb #PBS -l walltime=48:00:00 #work on current directory cd $PBS_O_WORKDIR #load java and set up memory settings to run nextflow module load java export NXF_OPTS='-Xms1g -Xmx4g' nextflow run nf-core/rnaseq --input samplesheet.csv \ --outdir results \ -r 3.12.0 \ --genome GRCm38-local \ -profile singularity \ --aligner star_salmon \ --extra_trimgalore_args "--quality 30 --clip_r1 10 --clip_r2 10 --three_prime_clip_r1 1 --three_prime_clip_r2 1 " |
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qsub launch_nf-core_RNAseq_pipeline.pbs |
qjobs |
The pipeline will produce two folders, one called “work,” where all the processing is done, and another called “results,” where we can find the pipeline's outputs. The content of the results folder is as follows:
results/
├── fastqc
├── multiqc
│ └── star_salmon
├── pipeline_info
├── star_salmon
│ ├── bigwig
│ ├── CD49fmNGFRm_rep1
│ ├── CD49fmNGFRm_rep2
│ ├── CD49fmNGFRm_rep3
│ ├── CD49fpNGFRp_rep1
│ ├── CD49fpNGFRp_rep2
│ ├── CD49fpNGFRp_rep3
│ ├── deseq2_qc
│ ├── dupradar
│ ├── featurecounts
│ ├── log
│ ├── MTEC_rep1
│ ├── MTEC_rep2
│ ├── MTEC_rep3
│ ├── picard_metrics
│ ├── qualimap
│ ├── rseqc
│ ├── samtools_stats
│ └── stringtie
└── trimgalore
└── fastqc |
The quantification of the gene and transcript expressions can be found in the ‘star_salmon’ directory.
cd results/star_salmon |
The following feature count tables are generated:
#gene level expression salmon.merged.gene_counts_length_scaled.rds salmon.merged.gene_counts_length_scaled.tsv salmon.merged.gene_counts.rds salmon.merged.gene_counts_scaled.rds salmon.merged.gene_counts_scaled.tsv salmon.merged.gene_counts.tsv <--- This file will be used for differential expression analysis using DESeq2 salmon.merged.gene_tpm.tsv #transcript level expression salmon.merged.transcript_counts.rds salmon.merged.transcript_counts.tsv salmon.merged.transcript_tpm.tsv |
Information on how to run a nextflow pipeline and additional available parameters can be provided on the pipeline website (i.e., https://nf-co.re/rnaseq/3.12.0/docs/usage/ ). You can also run the following command to get help information:
nextflow run nf-core/rnaseq --help |
Some pipelines may need file names, and others may want a CSV file with file names, the path to raw data files, and other information.