Page Comparison

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• Installed conda3 or miniconda3 ( https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html )

• Basic unix Unix command line knowledge (example: https://researchcomputing.princeton.edu/education/external-online-resources/linux ; https://swcarpentry.github.io/shell-novice/ )

  • https://sandbox.bio/

• Familiarity with one unix text editors (example Vi/Vim or Nano):

  • VIM ( https://bioinformatics.uconn.edu/vim-guide/ ; https://missing.csail.mit.edu/2020/editors/)

  • Nano (https://engineering.purdue.edu/ECN/Support/KB/Docs/BasictutorialforNanou ; https://www.howtogeek.com/howto/42980/the-beginners-guide-to-nano-the-linux-command-line-text-editor/ )

• Have an HPC account on QUT’s lyraHPC compute. Apply for a new HPC account here.

• R tutorials:

  • https://girke.bioinformatics.ucr.edu/GEN242/tutorials/

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The nf-core/rnaseq workflow requires Nextflow to be installed in your account on the HPC. Find details on how to install and test Nextflow here prepare . Prepare a cextflownextflow.config file and run a PBS pro submission script for Nextflow pipelines.

Additional information is available here: https://nf-co.re/usage/installation

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Usage: https://nf-co.re/rnaseq/3.0/usage

GithubGitHub: https://github.com/nf-core/rnaseq

Pipeline

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Summary

• Preprocessing

  • FastQC - Raw read QC

  • UMI-tools extract - UMI barcode extraction

  • TrimGalore - Adapter and quality trimming

  • SortMeRNA - Removal of ribosomal RNA (optional)

• Alignment and quantification

  • STAR and Salmon - Fast spliced aware genome alignment and transcriptome quantification

  • STAR via RSEM  - Alignment and quantification of expression levels

  • HISAT2  - Memory efficient splice aware alignment to a reference

• Alignment post-processing

  • SAMtools - Sort and index alignments

  • UMI-tools dedup - UMI-based deduplication

  • picard MarkDuplicates - Duplicate read marking

• Other steps

  • StringTie - Transcript assembly and quantification

  • BEDTools and bedGraphToBigWig - Create bigWig coverage files

• Quality control

  • RSeQC - Various RNA-seq QC metrics

  • Qualimap - Various RNA-seq QC metrics

  • dupRadar - Assessment of technical/biological read duplication

  • Preseq - Estimation of library complexity

  • featureCounts - Read counting relative to gene biotype

  • DESeq2 - PCA plot and sample pairwise distance heatmap and dendrogram

  • MultiQC - Present QC for raw reads, alignment, read counting, and sample similiaritysimilarity

• Pseudo-alignment and quantification

  • Salmon - Wicked fast gene and isoform quantification relative to the transcriptome

• Workflow reporting and genomes

  • Reference genome files - Saving reference genome indices/files

  • Pipeline information - Report metrics generated during the workflow execution

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Download and run the workflow using a minimal data provided by nf-core/rnaseq. We recommend using singularity as the profile for QUT’s HPC. Other Another profile option can be ‘conda’‘conda.’ Note: the profile option ‘docker’ is not available unavailable on the HPC.

nextflow run nf-core/rnaseq -profile test,singularity --outdir results -r 3.10.1

Running the pipeline using custom data

Prepare a sample sheet file that specifies the input files to be used. To do this, we use an nf-core script to generate the ‘samplesheet.csv’ file as follows:

#download script and make executable
wget -L https://raw.githubusercontent.com/nf-core/rnaseq/master/bin/fastq_dir_to_samplesheet.py
chmod +x fastq_dir_to_samplesheet.py

#generate the samplesheet.csv file
fastq_dir_to_samplesheet.py /path/to/directory/containing/fastq_files/ samplesheet.csv \
    --strandedness reverse \
    --read1_extension R1.fastq.gz \
    --read2_extension R2.fastq.gz

Example of a typical command to run a an RNA-seq analysis for mouse samples:

  nextflow run nf-core/rnaseq --input samplesheet.csv \
        ---input index.csv \outdir results \
        -r 3.10.1 \
        --genome GRCm38 GRCh38 \
        -profile singularity \
        --aligner star_salmon_rsem \
        --clip_r1 10 \
        --clip_r2 10 \
      -profile singularity  --three_prime_clip_r1 1 \
       -r 3.3 --three_prime_clip_r2 1

Note, if the running was interrupted or you did not complete a particular step, or you want to modify a parameter for a particular step, instead of re-running all process processes again, nextflow enables you to “-resume” the workflow.

  nextflow run nf-core/rnaseq --input samplesheet.csv \
        ---input index.csv \outdir results \
        -r 3.10.1 \
        --genome GRCm38 GRCh38 \
        -profile singularity \
        --aligner star_salmon \rsem \
        --clip_r1 10 \
        --clip_r2 10 \
        -profile singularity--three_prime_clip_r1 1 \
      -r 3.3  --three_prime_clip_r2 1 \
      -resume

Preparing a ‘samplesheet.csv’ file

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