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Public small RNA-seq data

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Code Block
mkdir -p $HOME/workshop/small_RNAseq/scripts cp /work/training/2024/smallRNAseq/scripts/* $HOME/workshop/small_RNAseq/scripts/ ls -l $HOME/workshop/small_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/training/2024/smallRNAseq/scripts/ as noted by an asterisk to the newly created folder $HOME/workshop/small_RNAseq/scripts/
Line 3: List the files in the script folder

Copy multiple subdirectories and files using rsync

Code Block
mkdir -p $HOME/workshop/small_RNAseq/data/ rsync -rv /work/training/2024/smallRNAseq/data/ $HOME/workshop/small_RNAseq/data/

Line 1: The first command creates the folder /data/
Line 2: rsync copies all subfolders and files from the specified source folder to the selected destination folder. The -r = recursively will copy directories and files; -v = verbose messages of the transfer of files

Create a folder for running the nf-core small RNA-seq pipeline

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Code Block
mkdir -p $HOME/workshop/small_RNAseq/run1_test mkdir -p $HOME/workshop/small_RNAseq/run2_smallRNAseq_human cd $HOME/workshop/small_RNAseq/

Lines 1-2: create sub-folders for each exercise
Line 3: change the directory to the folder “small_RNAseq”

Exercise 1: Running a test with nf-core sample data

First, let’s assess the execution of the nf-core/smrnaseq pipeline by running a test using sample data.

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Code Block
cat launch_nf-core_smallRNAseq_human_test.pbs

#!/bin/bash -l

#PBS -N sRNAseq_test

#PBS -l select=1:ncpus=2:mem=4gb

#PBS -l walltime=24:00:00

#PBS -m abe

cd $PBS_O_WORKDIR #work in current directory (folder)

module load java #load java and set up memory settings to run nextflow

export NXF_OPTS='-Xms1g -Xmx4g'

nextflow run nf-core/smrnaseq -r 2.3.1 \

-profile test,singularity \

--outdir results # run the test

where:

nextflow command: nextflow run
pipeline name: nf-core/smrnaseq
pipeline version: -r 2.3.1
container type and sample data: -profile test,singularity
output directory: --outdir results

Submitting the job

Now we can submit the small RNAseq test job to the HPC scheduler:

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Monitoring the Run

Code Block
qjobs

Exercise 2: Running the small RNA pipeline using public human data

The pipeline requires preparing at least 2 files:

Metadata file (samplesheet.csv) thatspecifies the “sample name” and “location of FASTQ files” ('Read 1').
PBS Pro script (launch_nf-core_smallRNAseq_human.pbs) with instructions to run the pipeline

How to create the metadata file (samplesheet.csv):

Change to the data folder directory:

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Code Block
cp /work/training/smallRNAseq/scripts/create_nf-core_smallRNAseq_samplesheet.sh $HOME/workshop/small_RNAseq/data/human

Note: you could replace ‘$HOME/workshop/small_RNAseq/data/human’ 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:

Code Block
cat create_nf-core_smallRNAseq_samplesheet.sh

#!/bin/bash -l

#User defined variables.

##########################################################

DIR='$HOME/workshop/small_RNAseq/data/human'

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.csv \

--strandedness auto \

--read1_extension .fastq.gz

#format index file

cat index.csv | awk -F "," '{print $1 "," $2}' > ${INDEX}

#Remove intermediate files:

rm index.csv fastq_dir_to_samplesheet.py

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Copy the PBS Pro script for running the full small RNAseq pipeline (launch_nf-core_smallRNAseq_human.pbs)

Copy and paste the code below to the terminal:

Code Block

cp $HOME/workshop/small_RNAseq/data/human/samplesheet.csv $HOME/workshop/small_RNAseq/run2_smallRNAseq_human
cp $HOME/workshop/small_RNAseq/scripts/launch_nf-core_smallRNAseq_human.pbs $HOME/workshop/small_RNAseq/run2_smallRNAseq_human
cd $HOME/workshop/small_RNAseq/run2_smallRNAseq_human

Line 1: Copy the samplesheet.csv file to the working directory
Line 2: Copy the launch_nf-core_smallRNAseq_human.pbs submission script to the working directory
Line 3: Move to the working directory

View the content of the launch_nf-core_RNAseq_QC.pbs script:

Code Block
cat launch_nf-core_smallRNAseq_human.pbs

#!/bin/bash -l

#PBS -N sRNAseq_human

#PBS -l select=1:ncpus=2:mem=4gb

#PBS -l walltime=24:00:00

#PBS -m abe

cd $PBS_O_WORKDIR #run the tasks in the current working directory

module load java #load java and assign up to 4GB RAM memory for nextflow to use

export NXF_OPTS='-Xms1g -Xmx4g'

nextflow run nf-core/smrnaseq -r 2.3.1 \

-profile singularity \

--outdir results \

--input samplesheet.csv \

--genome GRCh38-local \

--mirtrace_species hsa \

--three_prime_adapter 'TGGAATTCTCGGGTGCCAAGG' \

--fastp_min_length 18 \

--fastp_max_length 30 \

--hairpin /work/training/smallRNAseq/data/mirbase/hairpin.fa \

--mature /work/training/smallRNAseq/data/mirbase/mature.fa \

--mirna_gtf /work/training/smallRNAseq/data/mirbase/hsa.gff3 \

-resume #run the small RNAseq pipeline

Submit the job to the HPC cluster:

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Code Block
/work/training/2024/smallRNAseq/runs/run2_smallRNAseq_human

The results of the miRNA profiling can be found in the folder call “edger”“mirna_quant/edger_qc”:

Code Block

├── results/
│   ├── bowtie_index
edger│   ├── fastp
│   ├── fastqc
├── genome│   ├── index
├── mirdeepmirna_quant
│   ├── mirdeep2mirtrace
├── mirtop| ├── mirtrace ├── multiqc
├──| pipeline_info ├── samtools └── unmappedpipeline_info

inside the “edger” “mirna_quant/edger_qc” folder find the “mature_counts.csv” file:

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Note: the “mature_counts.csv” needs to be transposed prior running the statistical analysis. This can be done either user the R script or using a script called “transpose_csv.py”.

Let’s initially create a “DESeq2” folder and copy the files needed for the statistical analysis:

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Code Block
python transpose_csv.py --input mature_counts.csv --out mature_counts.txt

Differential expression analysis using RStudio

Differential expression analysis for smRNA-Seq is similar to regular RNA-Seq. Since you have already done the step-wise analysis in session 3, in this session we will streamline the analysis by running a single R script.

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a. Open Windows Explorer.

b. Go to: H:\workshop\small_RNAseq

c. Create a new folder here called ‘DESeq2’ (NOTE: R is case-sensitive, so it must be named exactly like this)

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c. Hit the save button and save this file in the working directory you created above (H:\workshop\small_RNAseq\DESeq2). Name the R script ‘DESeq2.R’.

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Using R Studio, create a Text File and paste in the contents of this script.

Save it as launch_R.pbs in H:\workshop\small_RNAseq\DESeq2 (Same folder as DESeq2.R (Remember, H: is pointed at your HPC Home Folder.

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Version	Old Version 2	New Version 3
Changes made by	Vicki Thomson	Vicki Thomson
Saved on	Aug 23, 2024	Aug 23, 2024

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Key

Public small RNA-seq data

Exercise 1: Running a test with nf-core sample data

Submitting the job

Monitoring the Run

Exercise 2: Running the small RNA pipeline using public human data

How to create the metadata file (samplesheet.csv):

Differential expression analysis using RStudio