Introduction Usage Parameters 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 and preprocessing
  • HUMANn v3 / v4 - Functional profiling via MetaPhlAn + HUMANn
  • FMH FunProfiler - Sketch-based functional profiling
  • mifaser - Read-level functional profiling
  • DIAMOND blastx - Translated alignment against a protein database (work in progress / beta)
  • EggNOG-mapper - Functional annotation via orthology assignment (work in progress / beta)
  • RGI BWT - Antimicrobial resistance gene identification
  • MultiQC - Aggregate report describing results and QC from the whole pipeline
  • Pipeline information - Report metrics generated during the workflow execution
Note

Other than FastQC, MultiQC and pipeline information, all other steps (the profilers) are off by default, and must be switched on manually.

FastQC

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

  • fastqc/
    • *_fastqc.html: FastQC report containing quality metrics for each sample.
    • *_fastqc.zip: Zip archive containing the FastQC report and data files.

HUMANn v3 / v4

HUMANn (HMP Unified Metabolic Analysis Network) is a method for efficiently and accurately profiling the presence/absence and abundance of microbial pathways in a community from metagenomic or metatranscriptomic sequencing data. Enabled with --run_humann_v3 or --run_humann_v4. Each sample is first run through MetaPhlAn to generate a taxonomic profile, which guides HUMANn functional profiling.

  • humann_v3/<sample>/ or humann_v4/<sample>/
    • *_genefamilies.tsv: Gene family abundances in reads per kilobase (RPK), stratified by contributing species.
    • *_pathabundance.tsv: Metabolic pathway abundances in RPK, stratified by species contribution.
    • *_pathcoverage.tsv: Pathway coverage scores (0–1), indicating the fraction of reactions detected per pathway.
  • metaphlan/<sample>/
    • *_profile.txt: Species-level taxonomic abundance profile used as input to HUMANn.

FMH FunProfiler

FMH FunProfiler uses FracMinHash sketching to rapidly assign reads to KEGG Orthology (KO) functional categories. Enabled with --run_fmhfunprofiler.

  • fmhfunprofiler/<sample>/
    • *.fmhfunprofiler.ko: KO (KEGG Orthology) abundance table for the sample.

mifaser

mifaser maps reads to functional databases at the protein level to produce enzyme function profiles. Enabled with --run_mifaser.

  • mifaser/<db_name>/<sample>/
    • analysis.tsv: Tab-separated table of functional assignments with read counts per enzyme function (EC number).
    • analysis.log: Log file with run statistics including number of reads processed and assigned.

DIAMOND blastx

Enabled with --run_diamond. Performs fast translated alignment of metagenomic reads against a protein reference database. Each read is aligned in all six reading frames and only significant hits are reported.

Warning

DIAMOND support is currently in beta and should be treated as work in progress. The module is still being validated in the full pipeline, including database handling, output behavior, and downstream reporting. Use with caution and independently review results before production use or interpretation.

Output files
  • diamond/<db_name>/
    • *.tsv: Tabular alignment results (BLAST tabular format 6) with one row per query-subject hit.
    • *.log: DIAMOND run log containing alignment statistics (query count, alignment rate, etc.).

EggNOG-mapper

Enabled with --run_eggnogmapper. Assigns functional annotations to sequences by mapping them to orthologous groups in the EggNOG database.

Warning

EggNOG-mapper support is currently in beta and should be treated as work in progress. The module is still being validated in the full pipeline, including database handling, output behavior, and downstream reporting. Use with caution and independently review results before production use or interpretation.

Output files
  • eggnogmapper/<db_name>/
    • *.emapper.annotations: TSV file with functional annotations per query sequence, including GO terms, KEGG pathways, COG categories, and more.
    • *.emapper.seed_orthologs: TSV linking query sequences to their best seed orthologs (optional, produced when search is performed).
    • *.emapper.hits: TSV with raw search hits from the search phase.

RGI BWT

RGI (Resistance Gene Identifier) aligns reads against the CARD database using Bowtie2/BWA to identify antimicrobial resistance genes. Enabled with --run_rgi.

  • rgi/<db_name>/
    • *.txt: Tab-separated AMR gene hit table with gene family, resistance mechanism, drug class, and read counts.
    • *.json: Full RGI output in JSON format with detailed per-hit annotations.

MultiQC

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.

  • 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.

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

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.

  • 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.
    • Parameters used by the pipeline run: params.json.
On this page