Aim:
Assess the quality of raw datasets
Define quality trimming parameters prior RNAseq gene profiling
Prepare Working Directory Space for Session 4 |
|---|
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/2024/rnaseq/scripts cp /work/training/2024/rnaseq/scripts/* $HOME/workshop/2024/rnaseq/scripts/ ls -l $HOME/workshop/2024/rnaseq/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/2024/rnaseq/data cp /work/training/2024/rnaseq/data/* $HOME/workshop/2024/rnaseq/data/ # list the content of the $HOME/workshop/2024/rnaseq/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 “runs” folder in the ~/workshop/2024/rnaseq folder to run the nf-core/rnaseq pipeline. For example:
mkdir -p $HOME/workshop/2024/rnaseq/runs mkdir $HOME/workshop/2024/rnaseq/runs/run1_test mkdir $HOME/workshop/2024/rnaseq/runs/run2_QC mkdir $HOME/workshop/2024/rnaseq/runs/run3_RNAseq cd $HOME/workshop/2024/rnaseq/runs
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
Exercise 1: Run RNA-seq QC check
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
Create the metadata file (samplesheet.csv):
Change to the data folder directory:
cd $HOME/workshop/2024/rnaseq/data/
Copy the bash script to the working folder
cp /work/training/2024/rnaseq/scripts/create_samplesheet_nf-core_RNAseq.sh $HOME/workshop/2024/rnaseq/data/
Note: you could replace ‘$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 |
|---|
NOTE: when paired end data is available (R1 and R2 FASTQ files for each sample). Then use: --read1_extension R1.fastq.gz \ --read2_extension R2.fastq.gz NOTE: check the suffix of the FASTQ files to specify the extension (e.g., R1_001.fastq.gz or R1.fq.gz) |
|---|
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 SRR20622172,/work/training/rnaseq/data/SRR20622172.fastq.gz,,auto SRR20622173,/work/training/rnaseq/data/SRR20622173.fastq.gz,,auto SRR20622174,/work/training/rnaseq/data/SRR20622174.fastq.gz,,auto SRR20622175,/work/training/rnaseq/data/SRR20622175.fastq.gz,,auto SRR20622176,/work/training/rnaseq/data/SRR20622176.fastq.gz,,auto SRR20622177,/work/training/rnaseq/data/SRR20622177.fastq.gz,,auto SRR20622178,/work/training/rnaseq/data/SRR20622178.fastq.gz,,auto SRR20622179,/work/training/rnaseq/data/SRR20622179.fastq.gz,,auto SRR20622180,/work/training/rnaseq/data/SRR20622180.fastq.gz,,auto |
|---|
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/2024/rnaseq/data/samplesheet.csv $HOME/workshop/2024/rnaseq/runs/run2_QC cp $HOME/workshop/scripts/launch_nf-core_RNAseq_QC.pbs $HOME/workshop/2024/rnaseq/runs/run2_QC cd $HOME/workshop/2024/rnaseq/runs/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 3.14.0 \ --input samplesheet.csv \ --outdir results \ --genome GRCm38-local \ --skip_trimming \ --skip_alignment \ --skip_pseudo_alignment |
|---|
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.
Submitting the job
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
Monitoring the Run
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.
Once the pipeline has finished running - Assess the QC report:
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 |
|---|---|---|
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).