bulkrna

dalmolingroup/bulkrna: Output

Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

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

• FastQC - Raw read QC
• fastp - fastp reports (in HTML and JSON) about the trimming process
• FastQC_trimmed - Trimmed read QC - will be included in MultiQC results
• Kallisto - Kallisto quantification results
• TxImport - Aggregate quantification results at gene and transcript levels
• MultiQC - Aggregate report describing results and QC from the whole pipeline
• Pipeline information - Report metrics generated during the workflow execution

FastQC

Output files

• fastqc/
  • *_fastqc.html: FastQC report containing quality metrics.
  • *_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

NB: The FastQC plots displayed in the MultiQC report shows trimmed reads (see fastp).

fastp

fastp is an ultra-fast all-in-one FASTQ preprocessor. Used in this workflow for adapter trimming and low-quality sequence removal.

• fastp
  • *.html - HTML report generated by fastp
  • *.json - JSON report generated by fastp

Kallisto

kallisto is used here for aligning and quantifying targets against a reference transcriptome.

• kallisto
  • {sample_id} - Abundances quantified from said sample

TxImport

tximport is used here to summarize transcript and gene-level counts from Kallisto.

• tximport
  • gene_tpm.tsv - Gene-level estimates (transcript-per-million) for the samples
  • gene_counts.tsv - Gene-level estimates (raw counts) for the samples
  • transcript_tpm.tsv - Transcript-level estimates (transcript-per-million) for the samples
  • transcript_counts.tsv - Transcript-level estimates (raw counts) for the samples
  • transcript_scaled_tpm.tsv - Transcript-level estimates (transcript-per-million) for the samples, scaled up to library size (scaledTPM)
  • transcript_scaled_counts.tsv - Transcript-level estimates (raw counts) for the samples, scaled up to library size (scaledTPM)

MultiQC

Output files

• multiqc/
  • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
  • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
  • multiqc_plots/: directory containing static images from the report in various formats.

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Pipeline information

Output files

• pipeline_info/
  • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and pipeline_dag.dot/pipeline_dag.svg.
  • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter’s are used when running the pipeline.
  • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.

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