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Prepared by the eResearch Office, QUT.

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

Pipeline Summary

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The pipeline is built using Nextflow and processes data using the following steps:

• Preprocessing

  • cat - Merge re-sequenced FastQ files

  • FastQC - Raw read QC

  • UMI-tools extract - UMI barcode extraction

  • TrimGalore - Adapter and quality trimming

  • BBSplit - Removal of genome contaminants

  • 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 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 similaritysimiliarity

• 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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#delete the existing assests associated with the RNAseq pipeline:
cd ~/.nextflow/assets/nf-core
rm -r rnaseq/

#run again a test with the new version that you are testing, for example, version 3.10.1. See details on how to run a test above (under 'Getting Started')

Add output folders/files

sample data

Running the pipeline using custom data

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