Introduction

nf-core/nanometaepigenomics is a clinical-grade, end-to-end bioinformatics pipeline designed for Oxford Nanopore Technologies (ONT) long-read metagenomic sequencing. It couples high-accuracy long-read metagenomic de novo assembly and Metagenome-Assembled Genome (MAG) recovery with native bacterial epigenetic profiling (5mC/5hmC/6mA methylation calling and motif identification). Crucially, the pipeline leverages Nanomotif to link unbinned mobile genetic elements (plasmids and phages identified by geNomad) back to host bacterial MAGs via shared restriction-modification methylation motifs.

Pipeline Steps Overview

1. Raw Read QC & Basecalling:
   • Sequencing read QC (NanoPlot).
   • GPU-accelerated basecalling and modified basecalling with MM/ML tags (Dorado).
   • Adapter trimming (Porechop_ABI) and length/quality filtering (Filtlong).
2. 2-Stage Host Decontamination:
   • Stage 1: Human GRCh38 privacy screening and depletion (Minimap2 + Samtools).
   • Stage 2: Background food matrix / declared host depletion (Minimap2 + Mosdepth).
3. De Novo Assembly & MGE Prediction:
   • Long-read metagenome assembly (metaFlye).
   • Plasmid and viral element identification (geNomad).
4. Binning & MAG Profiling:
   • Metagenomic binning (MetaBAT2).
   • MAG quality and completeness assessment (CheckM2).
   • Taxonomic classification (GTDB-Tk).
5. Epigenomics & Plasmid-Host Linkage:
   • Modification pileup generation (Modkit).
   • De novo bacterial methylation motif discovery and plasmid-to-host genome association (Nanomotif).
6. Surveillance & Functional Annotation:
   • Antimicrobial resistance profiling (AMRFinderPlus).
   • Virulence factor screening (ABRICATE with VFDB) and plasmid replicon typing (PlasmidFinder).
   • Rapid prokaryotic genome functional annotation (Bakta).
7. Quality Control & Reporting:
   • Audit manifest generation (*_run_manifest.json) and clinical summary table (*_clinical_report.tsv).
   • Aggregate quality reports across all tools (MultiQC).

Usage

Note

If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1
SAMPLE_1,/path/to/reads/SAMPLE_1.fastq.gz
SAMPLE_2,/path/to/reads/SAMPLE_2.fastq.gz

Each row represents an ONT run with long reads (gzipped FASTQ). If starting directly from Oxford Nanopore raw signal data (.pod5), you can point the pipeline to your POD5 folder via --pod5_dir.

Now, you can run the pipeline using:

nextflow run nf-core/nanometaepigenomics \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

Warning

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/nanometaepigenomics was originally written by Jyotirmoy Das (@JD2112).

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #nanometaepigenomics channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.