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Create working folder and copy data

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cp /work/training/ONTvariants/data/* $HOME/workshop/ONTvariants/data
cp /work/training/ONTvariants/scripts/* $HOME/workshop/ONTvariants/scripts
cd $HOME/workshop/ONTvariants

Install tools using conda

Approach 1: Create a conda

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environment and install tools one at a time

Create a conda environment called ONTvariants_QC

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Let’s activate the conda environment:

conda activate ONTvariantONTvariants_QC

Next, we need to install few tools for today’s exercises. Now let’s go the https://anaconda.org and search for the following tools and instructions on how to install them:

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conda install bioconda::seqkit

Approach 2: Create environment and install tools all at once

This is a slower option, but it is convenient when installing many tools.

Prepare the following environment.yml file:

name: ONTvariants_QC
channels:
  - conda-forge
  - defaults
  - bioconda
dependencies:
  - nanoplot
  - porechop_abi
  - chopper

Create a new environment:

cd $HOME/workshop/ONTvariants/scripts
conda env create -f environment_QC.yml

Running QC

Now that we have installed all the tools needed for the QC of Nanopore reads, let’s run the preprocessing of reads.

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#!/bin/bash -l
#PBS -N run1_QC
#PBS -l select=1:ncpus=8:mem=16gb
#PBS -l walltime=48:00:00
#PBS -m abe

cd $PBS_O_WORKDIR

conda activate ONTvariants_QC

###############################################################
# Variables
###############################################################
FASTQ='/work/training/ONTvariants/data/SRR17138639_1.fastq.gz'
GENOME='/work/training/ONTvariants/data/chr20.fasta'
SAMPLEID='SRR17138639'
###############################################################

#STEP1: NanoPlot - overall QC report
NanoPlot -t 8 --fastq $FASTQ --prefix ${SAMPLEID}_QC_ --plots dot --N50 --tsv_stats

#STEP2: porechop_abi - remove adapters
porechop_abi -abi -t 8 --input ${SAMPLEID}.fastq.gz$FASTQ --discard_middle --output ${SAMPLEID}_trimmed.fastq

#STEP3: chopper - retain reads with >Q10 and length>300b
chopper -q 10 -l 300 -i ${SAMPLEID}_trimmed.fastq > ${SAMPLEID}_trimmed_q10.fastq

#STEP4: get stats of trimmed FASTQ files
seqkit stats *.fastq > Report_trimmed_FASTQ_stats.txt

Note:

• Line 1: Defines that the script is a bash script.

• Lines 2-5: Are commented out with “#” at the beginning and are ignored by bash, however, these PBS lines tell the scholar (PBS Pro) the name of the job (line 2), the number of CPUs and RAM memory to use (line 3), the time to run the script (line 4) and report if there are any errors (line 5).

• Line 7: Tells the job to run on the current directory.

• Line 9: Activate the conda environment where the QC tools were installed using conda.

• Lines 11-17: User defined variables. Modify the FASTQ, genome and/or sample ID to use to run the job as appropriate. Note: in the lines below, the variable names are used instead of the actual names or locations of the files (e.g., $FASTQ)

• Line 20: Run a Quality Control (QC) overview of the raw Nanopore reads using NanoPlot

• Line 23: Remove adapter sequences from the 5'- and 3’-ends of the raw reads

• Line 26: Filter reads with a quality score below Q10 (90% accuracy; -q 10) and shorter than 300 bases (-l 300)

• Line 28: collect the stats for trimmed FASTQ files processed using porechop_abi and chopper

Submit the QC job to the HPC cluster:

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As outputs find the porechop_abi processed file (SRR17138639_1_porechop_abi.fastq) and the chopper output (SRR17138639_1_porechop_abi_chopper_q10_300b.fastq). To visualise the QC reports, let’s connect to the HPC via file finder (see below).

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:

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Next, let’s inspect the “SRR17138639_QC_LengthvsQualityScatterPlot_dot.png“ file. Alternatively for high resolution image open instead “SRR17138639_QC_LengthvsQualityScatterPlot_dot.html“

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Next: ONTvariants - mapping