/funcscan

(Meta-)genome screening for functional and natural product gene sequences

Primary LanguageNextflowMIT LicenseMIT

nf-core/funscan nf-core/funscan

AWS CICite with Zenodo Nextflow run with conda run with docker run with singularity Launch on Nextflow Tower

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Introduction

nf-core/funcscan is a bioinformatics best-practice analysis pipeline for the screening of nucleotide sequences such as assembled contigs for functional genes. It currently features mining for antimicrobial peptides, antibiotic resistance genes and biosynthetic gene clusters.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

The nf-core/funcscan AWS full test dataset are contigs generated by the MGnify service from the ENA. We used contigs generated from assemblies of chicken cecum shotgun metagenomes (study accession: MGYS00005631).

Pipeline summary

  1. Annotation of assembled prokaryotic contigs with Prodigal, Prokka, or Bakta
  2. Screening contigs for antimicrobial peptide-like sequences with ampir, Macrel, HMMER, AMPlify
  3. Screening contigs for antibiotic resistant gene-like sequences with ABRicate, AMRFinderPlus, fARGene, RGI, DeepARG
  4. Screening contigs for biosynthetic gene cluster-like sequences with antiSMASH, DeepBGC, GECCO, HMMER
  5. Creating aggregated reports for all samples across the workflows with AMPcombi for AMPs, hAMRonization for ARGs, and comBGC for BGCs
  6. Software version and methods text reporting with MultiQC

funcscan metro workflow

Quick Start

  1. Install Nextflow (>=22.10.1)

  2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

  3. Download the pipeline and test it on a minimal dataset with a single command:

    nextflow run nf-core/funcscan -profile test,YOURPROFILE --outdir <OUTDIR>

    Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

    • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
    • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
    • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you to store and re-use the images from a central location for future pipeline runs.
    • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

  4. Start running your own analysis!

    nextflow run nf-core/funcscan --input samplesheet.csv --outdir <OUTDIR> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --run_amp_screening --run_arg_screening --run_bgc_screening

Documentation

The nf-core/funcscan pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

nf-core/funcscan was originally written by Jasmin Frangenberg, Anan Ibrahim, Louisa Perelo, Moritz E. Beber, and James A. Fellows Yates.

We thank the following people for their extensive assistance in the development of this pipeline:

Rosa Herbst, Martin Klapper.

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 #funcscan channel (you can join with this invite).

Citations

If you use nf-core/funcscan for your analysis, please cite it using the following doi: 10.5281/zenodo.7643099

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.