Introduction Usage Parameters Output

Introduction

This document describes the output produced by 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 renders Quarto reports from user-provided input files. It processes data using the following steps:

Quarto notebook reports

Output files
  • quartonotebook/
    • *.html: One rendered HTML report for the full samplesheet. The default filename is based on the notebook name, for example provenance_report.html.
    • *.qmd: The Quarto notebook used to generate the report. If --notebook supplies a custom notebook, the published notebook filename will match that file.

The reports are generated by the nf-core quartonotebook module. The workflow passes the selected notebook, a parameter map describing all samplesheet rows, and the staged input files into the module. This allows custom notebooks to read one or more files from the task working directory and render report-specific output.

The local REPORTENVIRONMENT module collects the Quarto version from the same report container and adds it to the pipeline software versions file under pipeline_info/. Intermediate helper files such as per-task params.yml files are not published because repeated report runs would otherwise write the same filename to the output directory.

Report artifacts

Output files
  • quartonotebook/
    • Files written by the notebook to params$artifact_dir, such as *_input_files.tsv, *_summary.txt, images, or tables.

Custom notebooks should write secondary output files to params$artifact_dir. The quartonotebook module emits files from that directory through its artifacts output, and the pipeline publishes them alongside the HTML reports. Use stable artifact filenames when writing custom output files.

MultiQC execution report

Output files
  • multiqc/
    • multiqc_report.html: Standalone pipeline execution and provenance report.
    • multiqc_data/: Machine-readable data used to build the report.

The MultiQC report is generated with the nf-core multiqc module and is configured as a report in tower.yml, allowing Seqera Platform to display it in the run Reports tab. It contains:

  • The validated input samplesheet and workflow parameter summary.
  • The exact pipeline launch command and a description of the report-generation steps.
  • The resolved --report_container reference shared by QUARTONOTEBOOK and REPORTENVIRONMENT, the container engine, the active Nextflow profile, R sessionInfo() output, and Python version. R and Python are reported as unavailable when they are absent from the report container.
  • Pipeline, Nextflow, and Quarto versions.

nf-prov provenance

Output files
  • nf-prov/
    • bco.json: BioCompute Object provenance report.
    • ro-crate-metadata.json: Workflow Run RO-Crate metadata generated by nf-prov.
    • Supporting files for the Workflow Run RO-Crate output, such as README.md, main.nf, nextflow.config, and samplesheet.csv.

The nf-prov plugin creates provenance reports at the end of the run. These files are written to a dedicated nf-prov/ directory to keep them separate from standard Nextflow execution metadata.

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 nf_core_provenancereport_software_mqc_versions.yml. The pipeline_report* files will only be present if the --email or --email_on_fail parameters are used when running the pipeline.
    • Validated samplesheet information is used internally to create one report task containing all listed files.
    • Parameters used by the pipeline run: params.json.

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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