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Public small RNA-seq data

Species	ENA link	Description
Human	https://www.ebi.ac.uk/ena/browser/view/PRJEB5212?show=publications	RNA-seq of micro RNAs (miRNAs) in Human prefrontal cortex to identify differentially expressed miRNAs between Huntington's Disease and control brain samples

1. Connect to an rVDI virtual desktop machine

To access and run an rVDI virtual desktop:

Go to https://rvdi.qut.edu.au/

Click on ‘VMware Horizon HTML Access’

Log on with your QUT username and password

*NOTE: you need to be connected to the QUT network first, either being on campus or connecting remotely via VPN.

2. Open PuTTY terminal

Click on the PuTTY icon
Double click on “Lyra”
Fill your password and connect to the HPC

Copying data for hands-on exercises

Before we start using the HPC, let’s start an interactive session:

Code Block
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:

Code Block
mkdir -p $HOME/workshop/small_RNAseq/scripts cp /work/training/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/datasets/workshop/scripts/ as noted by an asterisk to the newly created folder $HOME/workshop/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/smallRNAseq/data/ $HOME/workshop/small_RNAseq/data/

Line 1: The first command creates the folder /scripts/
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

Let’s create a “runs” folder to run the nf-core/rnaseq pipeline:

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

Lines 1-4: create sub-folders for each exercise
Line 5: 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/rnaseq pipeline by running a test using sample data.

Copy the launch_nf-core_smallRNAseq_test.pbs to the working directory

Code Block
cd $HOME/workshop/small_RNAseq/run1_test cp $HOME/workshop/small_RNAseq/scripts/launch_nf-core_smallRNAseq_test.pbs .

View the content of the script as follows:

Code Block
cat launch_nf-core_smallRNAseq_test.pbs

#!/bin/bash -l

#PBS -N nfsmrnaseq

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

#PBS -l walltime=24:00:00

#work on current directory (folder)

cd $PBS_O_WORKDIR

#load java and set up memory settings to run nextflow

module load java

export NXF_OPTS='-Xms1g -Xmx4g'

# run the test

nextflow run nf-core/smrnaseq -profile test,singularity --outdir results -r 2.1.0

where:

nextflow command: nextflow run
pipeline name: nf-core/smrnaseq
pipeline version: -r 2.1.0
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:

Code Block
qsub launch_nf-core_smallRNAseq_test.pbs

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

Create the metadata file (samplesheet.csv):

Change to the data folder directory:

Code Block
cd $HOME/workshop/small_RNAseq/data/human pwd

Copy the bash script to the working folder

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/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:

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

Let’s generate the metadata file by running the following command:

Code Block
sh create_RNAseq_samplesheet.sh

Check the newly created samplesheet.csv file:

Code Block
ls -l cat samplesheet.cvs

sample,fastq_1

SRR20753704,/work/training/smallRNAseq/data/SRR20753704.fastq.gz

SRR20753705,/work/training/smallRNAseq/data/SRR20753705.fastq.gz

SRR20753706,/work/training/smallRNAseq/data/SRR20753706.fastq.gz

SRR20753707,/work/training/smallRNAseq/data/SRR20753707.fastq.gz

SRR20753708,/work/training/smallRNAseq/data/SRR20753708.fastq.gz

SRR20753709,/work/training/smallRNAseq/data/SRR20753709.fastq.gz

SRR20753716,/work/training/smallRNAseq/data/SRR20753716.fastq.gz

SRR20753717,/work/training/smallRNAseq/data/SRR20753717.fastq.gz

SRR20753718,/work/training/smallRNAseq/data/SRR20753718.fastq.gz

SRR20753719,/work/training/smallRNAseq/data/SRR20753719.fastq.gz

SRR20753720,/work/training/smallRNAseq/data/SRR20753720.fastq.gz

SRR20753721,/work/training/smallRNAseq/data/SRR20753721.fastq.gz

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 nfsmallRNAseq

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

#PBS -l walltime=24:00:00

#PBS -m abe

#run the tasks in the current working directory

cd $PBS_O_WORKDIR

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

module load java

export NXF_OPTS='-Xms1g -Xmx4g'

#run the small RNAseq pipeline

nextflow run nf-core/smrnaseq -r 2.1.0 \

-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

Submit the job to the HPC cluster:

Code Block
qsub launch_nf-core_smallRNAseq_human.pbs

Monitor the progress:

Code Block
qjobs

The job will take several hours to run, hence we will use precomputed results for the statistical analysis in the next section.

Precomputed results:

We ran the small RNA seq samples and the results can be found at:

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

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

Code Block

results/
├── edger
├── fastp
├── fastqc
├── genome
├── index
├── mirdeep
├── mirdeep2
├── mirtop
├── mirtrace
├── multiqc
├── pipeline_info
├── samtools
└── unmapped

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

Code Block

hairpin_counts.csv
hairpin_CPM_heatmap.pdf
hairpin_edgeR_MDS_distance_matrix.txt
hairpin_edgeR_MDS_plot_coordinates.txt
hairpin_edgeR_MDS_plot.pdf
hairpin_log2CPM_sample_distances_dendrogram.pdf
hairpin_log2CPM_sample_distances_heatmap.pdf
hairpin_log2CPM_sample_distances.txt
hairpin_logtpm.csv
hairpin_logtpm.txt
hairpin_normalized_CPM.txt
hairpin_unmapped_read_counts.txt
mature_counts.csv       <-- we will use this file for the statistical analysis in the next section
mature_counts.txt
mature_CPM_heatmap.pdf
mature_edgeR_MDS_distance_matrix.txt
mature_edgeR_MDS_plot_coordinates.txt
mature_edgeR_MDS_plot.pdf
mature_log2CPM_sample_distances_dendrogram.pdf
mature_log2CPM_sample_distances_heatmap.pdf
mature_log2CPM_sample_distances.txt
mature_logtpm.csv
mature_logtpm.txt
mature_normalized_CPM.txt
mature_unmapped_read_counts.txt

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:

Code Block

mkdir -p $HOME/workshop/small_RNAseq/DESeq2
cp $HOME/workshop/small_RNAseq/scripts/transpose_csv.py $HOME/workshop/small_RNAseq/DESeq2
cp $HOME/workshop/small_RNAseq/data/human/metadata_microRNA.txt $HOME/workshop/small_RNAseq/DESeq2
cp /work/training/smallRNAseq/runs/deprecated/run2_smallRNAseq_human/results/edger/mature_counts.csv $HOME/workshop/small_RNAseq/DESeq2
cd $HOME/workshop/small_RNAseq/DESeq2

To transpose the initial “mature_counst.csv” file do the following:

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

Versions Compared

Old Version 3

New Version 4

Key

Public small RNA-seq data

1. Connect to an rVDI virtual desktop machine

2. Open PuTTY terminal

Copying data for hands-on exercises

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

Create the metadata file (samplesheet.csv):

Precomputed results: