(Meta-)genome screening for functional and natural product gene sequences
*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).
- Annotation of assembled prokaryotic contigs with
- Screening contigs for antimicrobial peptide-like sequences with
- Screening contigs for antibiotic resistant gene-like sequences with
- Screening contigs for biosynthetic gene cluster-like sequences with
- Creating aggregated reports for all samples across the workflows with
hAMRonizationfor ARGs, and
- Software version and methods text reporting with
First, prepare a samplesheet with your input data that looks as follows:
sample,fasta CONTROL_REP1,AEG588A1_001.fasta CONTROL_REP2,AEG588A1_002.fasta CONTROL_REP3,AEG588A1_003.fasta
Each row represents a (multi-)fasta file of assembled contig sequences.
Now, you can run the pipeline using:
nextflow run nf-core/funcscan \ -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> \ --input samplesheet.csv \ --outdir <OUTDIR> \ --run_amp_screening \ --run_arg_screening \ --run_bgc_screening
Please provide pipeline parameters via the CLI or Nextflow
-params-fileoption. Custom config files including those
provided by the
-cNextflow option can be used to provide any configuration *except for parameters*;
To see the the results of a 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
nf-core/funcscan was originally written by Jasmin Frangenberg, Anan Ibrahim, Louisa Perelo, Moritz E. Beber, 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.
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
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.