/nf-mgx-short

Nextflow Pipeline to Run Taxonomic Classifier, Functional Annotation and Resistome Analysis for Short Shotgun Metagenomics Reads

Primary LanguageNextflow

Shotgun Metagenomics Nextflow Pipeline

Introduction

indrikwijaya/nf-mgx-short is a bioinformatics pipeline that takes raw metagenomic (short reads) (MGX) and annotates them at the taxonomic, functional and resistome levels.

The pipeline is built using Nextflow DSL2 , a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker containers (also compatible with Singularity) for ease of installation and computational reproducibility.

This pipeline currently only accepts paired-end reads as inputs.

Pipeline summary for metagenomic reads

  1. Adapter trimming and quality control using fastp (0.22.0)

  2. Removal of host (human) reads by mapping to a reference genome using bwa (0.7.17)

  3. Taxonomic classification of non-human reads

    a. using Kraken2 (2.1.2) and taxonomic abundance re-estimation using Bracken (2.6.1)

    b. using Metaphlan4 (4.0.6)

  4. Functional annotation of reads using HUMANN3

  5. Resistome annotation of reads using SRST2

Quick Start

  1. Install Nextflow (>=21.04.0) and add the nextflow executable to your $PATH

  2. Install Docker

  3. Clone the pipeline and refer to the help message

    $ git clone https://github.com/indrikwijaya/nf-mgx-short

$ nextflow run ./nf-mgx-short/main.nf --help
  • Add a custom config file which contains the paths to various pre-installed databases. Refer to the test.config file in this repo for an example.
  • Add a custom profile in the nextflow.config file, allowing you to specify the use of docker or singularity, and/or a task scheduler.

  1. Make sure all helper scripts in nf-mgx-short/bin have execute permissions

    $ cd bin/
$ chmod +x ./nf-mgx-short/bin/*

  2. Run the pipeline

    $ nextflow run ./nf-mgx-short/main.nf -profile docker,your_profile --profilers metaphlan4 --read_path /path/to/metagenomes --outdir /path/to/results
  • You can specifiy multiple profiles separated by comma, e.g. -profile docker,sge.
  • The taxonomic classification, nucleotide alignment, translated search and annotation modules can be quite memory intensive depending on the databases used
  • Delete the work/ directory after running the pipeline to free up space taken up by intermediate files

Contact: Indrik Wijaya indrik_wijaya@gis.a-star.edu.sg, indrik.wijaya@gmail.com