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funcscan

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

https://github.com/nf-core/funcscan

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    Found 10 DOI reference(s) in README
  • ○
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    1 of 14 committers (7.1%) from academic institutions
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    Low similarity (10.8%) to scientific vocabulary

Keywords

amp amr antibiotic-resistance antimicrobial-peptides antimicrobial-resistance-genes arg assembly bgc bioinformatics biosynthetic-gene-clusters contigs function metagenomics natural-products nextflow nf-core pipeline screening secondary-metabolites workflow

Keywords from Contributors

pipelines workflows dsl2 nf-test taxonomic-classification nanopore synapse sra geo fastq
Last synced: 4 months ago · JSON representation ·

Repository

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

Basic Info
  • Host: GitHub
  • Owner: nf-core
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://nf-co.re/funcscan
  • Size: 25.6 MB
Statistics
  • Stars: 92
  • Watchers: 114
  • Forks: 33
  • Open Issues: 26
  • Releases: 11
Topics
amp amr antibiotic-resistance antimicrobial-peptides antimicrobial-resistance-genes arg assembly bgc bioinformatics biosynthetic-gene-clusters contigs function metagenomics natural-products nextflow nf-core pipeline screening secondary-metabolites workflow
Created about 4 years ago · Last pushed 4 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/funcscan

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

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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. Quality control of input sequences with SeqKit
  2. Taxonomic classification of contigs of prokaryotic origin with MMseqs2
  3. Annotation of assembled prokaryotic contigs with Prodigal, Pyrodigal, Prokka, or Bakta
  4. Annotation of coding sequences from 3. to obtain general protein families and domains with InterProScan
  5. Screening contigs for antimicrobial peptide-like sequences with ampir, Macrel, HMMER, AMPlify
  6. Screening contigs for antibiotic resistant gene-like sequences with ABRicate, AMRFinderPlus, fARGene, RGI, DeepARG. argNorm is used to map the outputs of DeepARG, AMRFinderPlus, and ABRicate to the Antibiotic Resistance Ontology for consistent ARG classification terms.
  7. Screening contigs for biosynthetic gene cluster-like sequences with antiSMASH, DeepBGC, GECCO, HMMER
  8. Creating aggregated reports for all samples across the workflows with AMPcombi for AMPs, hAMRonization for ARGs, and comBGC for BGCs
  9. Software version and methods text reporting with MultiQC

funcscan metro workflow

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:

csv 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:

bash 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

[!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/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:

Adam Talbot, Alexandru Mizeranschi, Hugo Tavares, Júlia Mir Pedrol, Martin Klapper, Mehrdad Jaberi, Robert Syme, Rosa Herbst, Vedanth Ramji, @Microbion.

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.

Owner

  • Name: nf-core
  • Login: nf-core
  • Kind: organization
  • Email: core@nf-co.re

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md) 

# nf-core/funcscan: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature biotechnology, 38(3), 276–278. [DOI: 10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature biotechnology, 35(4), 316–319. [DOI: 10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)

## Pipeline tools

- [ABRicate](https://github.com/tseemann/abricate)

  > Seemann, T. (2020). ABRicate. Github [https://github.com/tseemann/abricate](https://github.com/tseemann/abricate).

- [AMPir](https://doi.org/10.1093/bioinformatics/btaa653)

  > Fingerhut, L., Miller, D. J., Strugnell, J. M., Daly, N. L., & Cooke, I. R. (2021). ampir: an R package for fast genome-wide prediction of antimicrobial peptides. Bioinformatics (Oxford, England), 36(21), 5262–5263. [DOI: 10.1093/bioinformatics/btaa653](https://doi.org/10.1093/bioinformatics/btaa653)

- [AMPlify](https://doi.org/10.1186/s12864-022-08310-4)

  > Li, C., Sutherland, D., Hammond, S. A., Yang, C., Taho, F., Bergman, L., Houston, S., Warren, R. L., Wong, T., Hoang, L., Cameron, C. E., Helbing, C. C., & Birol, I. (2022). AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC genomics, 23(1), 77. [DOI: 10.1186/s12864-022-08310-4](https://doi.org/10.1186/s12864-022-08310-4)

- [AMRFinderPlus](https://doi.org/10.1038/s41598-021-91456-0)

  > Feldgarden, M., Brover, V., Gonzalez-Escalona, N., Frye, J. G., Haendiges, J., Haft, D. H., Hoffmann, M., Pettengill, J. B., Prasad, A. B., Tillman, G. E., Tyson, G. H., & Klimke, W. (2021). AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Scientific reports, 11(1), 12728. [DOI: 10.1038/s41598-021-91456-0](https://doi.org/10.1038/s41598-021-91456-0)

- [AntiSMASH](https://doi.org/10.1093/nar/gkad344)

  > Blin, K., Shaw, S., Augustijn, H. E., Reitz, Z. L., Biermann, F., Alanjary, M., Fetter, A., Terlouw B. R., Metcalf, W. W., Helfrich, E. J. N., van Wezel, G. P., Medema, M. H., & Weber, T. (2023). antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic acids research, 51(W1), W46–W50. [DOI: 10.1093/nar/gkad344](https://doi.org/10.1093/nar/gkad344)

- [argNorm](https://doi.org/10.5204/rep.eprints.252448)

  > Ugarcina Perovic, S., Ramji, V., Chong, H., Duan, Y., Maguire, F., Coelho, L. P. (2024). argNorm: Normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO). [Preprint] (Unpublished) [DOI: 10.5204/rep.eprints.252448](https://doi.org/10.5204/rep.eprints.252448)

- [Bakta](https://doi.org/10.1099/mgen.0.000685)

  > Schwengers, O., Jelonek, L., Dieckmann, M. A., Beyvers, S., Blom, J., & Goesmann, A. (2021). Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microbial Genomics, 7(11). [DOI: 10.1099/mgen.0.000685](https://doi.org/10.1099/mgen.0.000685)

- [comBGC](https://github.com/nf-core/funcscan)

  > Frangenberg, J., Fellows Yates, J. A., Ibrahim, A., Perelo, L., & Beber, M. E. (2023). nf-core/funcscan: 1.0.0 - German Rollmops - 2023-02-15. [DOI: 10.5281/zenodo.7643100](https://doi.org/10.5281/zenodo.7643099)

- [DeepARG](https://doi.org/10.1186/s40168-018-0401-z)

  > Arango-Argoty, G., Garner, E., Pruden, A., Heath, L. S., Vikesland, P., & Zhang, L. (2018). DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome, 6(1), 23. [DOI: 10.1186/s40168-018-0401-z](https://doi.org/10.1186/s40168-018-0401-z)

- [DeepBGC](https://doi.org/10.1093/nar/gkz654)

  > Hannigan, G. D., Prihoda, D., Palicka, A., Soukup, J., Klempir, O., Rampula, L., Durcak, J., Wurst, M., Kotowski, J., Chang, D., Wang, R., Piizzi, G., Temesi, G., Hazuda, D. J., Woelk, C. H., & Bitton, D. A. (2019). A deep learning genome-mining strategy for biosynthetic gene cluster prediction. Nucleic acids research, 47(18), e110. [DOI: 10.1093/nar/gkz654](https://doi.org/10.1093/nar/gkz654)

- [fARGene](https://doi.org/10.1186/s40168-019-0670-1)

  > Berglund, F., Österlund, T., Boulund, F., Marathe, N. P., Larsson, D., & Kristiansson, E. (2019). Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome, 7(1), 52. [DOI: 10.1186/s40168-019-0670-1](https://doi.org/10.1186/s40168-019-0670-1)

- [GECCO](https://gecco.embl.de)

  > Carroll, L. M., Larralde, M., Fleck, J. S., Ponnudurai, R., Milanese, A., Cappio Barazzone, E. & Zeller, G. (2021). Accurate de novo identification of biosynthetic gene clusters with GECCO. bioRxiv. [DOI: 10.1101/2021.05.03.442509](https://doi.org/10.1101/2021.05.03.442509)

- [AMPcombi](https://github.com/Darcy220606/AMPcombi)

  > Ibrahim, A. & Perelo, L. (2023). Darcy220606/AMPcombi. [DOI: 10.5281/zenodo.7639121](https://doi.org/10.5281/zenodo.7639121).

- [hAMRonization](https://github.com/pha4ge/hAMRonization)

  > Mendes, I., Griffiths, E., Manuele, A., Fornika, D., Tausch, S. H., Le-Viet, T., Phelan, J., Meehan, C. J., Raphenya, A. R., Alcock, B., Culp, E., Lorenzo, F., Haim, M. S., Witney, A., Black, A., Katz, L., Oluniyi, P., Olawoye, I., Timme, R., Neoh, H., Lam, S. D., Jamaluddin, T. Z. M. T., Nathan, S., Ang, M. Y., Di Gregorio, S., Vandelannoote, K., Dusadeepong, R, Chindelevitch, L., Nasar, M. I., Aanensen, D., Afolayan, A. O., Odih, E. E., McArthur, A. G., Feldgarden, M., Galas, M. M., Campos, J., Okeke, I. N., Underwood, A., Page, A. J., MacCannell, D., Maguire, F. (2023). hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling. bioRxiv. [DOI: 10.1101/2024.03.07.583950](https://doi.org/10.1101/2024.03.07.583950)

- [HMMER](https://doi.org/10.1371/journal.pcbi.1002195.)

  > Eddy S. R. (2011). Accelerated Profile HMM Searches. PLoS computational biology, 7(10), e1002195. [DOI: 10.1371/journal.pcbi.1002195](https://doi.org/10.1371/journal.pcbi.1002195)

- [InterPro](https://doi.org/10.1093/nar/gkaa977)

  > Blum, M., Chang, H-Y., Chuguransky, S., Grego, T., Kandasaamy, S., Mitchell, A., Nuka, G., Paysan-Lafosse, T., Qureshi, M., Raj, S., Richardson, L., Salazar, G. A., Williams, L., Bork, P., Bridge, A., Gough, J., Haft, D. H., Letunic, I., Marchler-Bauer, A., Mi, H., Natale, D. A., Necci, M., Orengo, C. A., Pandurangan, A. P., Rivoire, C., Sigrist, C. A., Sillitoe, I., Thanki, N., Thomas, P. D., Tosatto, S. C. E, Wu, C. H., Bateman, A., Finn, R. D. (2021) The InterPro protein families and domains database: 20 years on. Nucleic Acids Research, 49(D1), D344–D354. [DOI: 10.1093/nar/gkaa977](https://doi.org/10.1093/nar/gkaa977)

- [InterProScan](https://doi.org/10.1093/bioinformatics/btu031)

  > Jones, P., Binns, D., Chang, H-Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., Pesseat, S., Quinn, A. F., Sangrador-Vegas, A., Scheremetjew, M., Yong, S-Y., Lopez, R., Hunter, S. (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics, 30(9), 1236–1240. [DOI: 10.1093/bioinformatics/btu031](https://doi.org/10.1093/bioinformatics/btu031)

- [Macrel](https://doi.org/10.7717/peerj.10555)

  > Santos-Júnior, C. D., Pan, S., Zhao, X. M., & Coelho, L. P. (2020). Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ, 8, e10555. [DOI: 10.7717/peerj.10555](https://doi.org/10.7717/peerj.10555)

- [MMseqs2](https://doi.org/10.1093/bioinformatics/btab184)

  > Mirdita, M., Steinegger, M., Breitwieser, F., Söding, J., Levy Karin, E. (2021). Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics, 37(18),3029–3031. [DOI: 10.1093/bioinformatics/btab184](https://doi.org/10.1093/bioinformatics/btab184)

- [Prodigal](https://doi.org/10.1186/1471-2105-11-119)

  > Hyatt, D., Chen, G. L., Locascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics, 11, 119. [DOI: 10.1186/1471-2105-11-119](https://doi.org/10.1186/1471-2105-11-119)

- [PROKKA](https://doi.org/10.1093/bioinformatics/btu153)

  > Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics (Oxford, England), 30(14), 2068–2069. [DOI: 10.1093/bioinformatics/btu153](https://doi.org/10.1093/bioinformatics/btu153)

- [Pyrodigal](https://doi.org/10.1186/1471-2105-11-119)

  > Larralde, M. (2022). Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. Journal of Open Source Software, 7(72), 4296. [DOI: 10.21105/joss.04296](https://doi.org/10.21105/joss.04296)

- [RGI](https://doi.org/10.1093/nar/gkac920)

  > Alcock, B. P., Huynh, W., Chalil, R., Smith, K. W., Raphenya, A. R., Wlodarski, M. A., Edalatmand, A., Petkau, A., Syed, S. A., Tsang, K. K., Baker, S. J. C., Dave, M., McCarthy, M. C., Mukiri, K. M., Nasir, J. A., Golbon, B., Imtiaz, H., Jiang, X., Kaur, K., Kwong, M., Liang, Z. C., Niu, K. C., Shan, P., Yang, J. Y. J., Gray, K. L., Hoad, G. R., Jia, B., Bhando, T., Carfrae, L. A., Farha, M. A., French, S., Gordzevich, R., Rachwalski, K., Tu, M. M., Bordeleau, E., Dooley, D., Griffiths, E., Zubyk, H. L., Brown, E. D., Maguire, F., Beiko, R. G., Hsiao, W. W. L., Brinkman F. S. L., Van Domselaar, G., McArthur, A. G. (2023). CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic acids research, 51(D1):D690-D699. [DOI: 10.1093/nar/gkac920](https://doi.org/10.1093/nar/gkac920)

- [SeqKit](https://bioinf.shenwei.me/seqkit/)

  > Shen, W., Sipos, B., & Zhao, L. (2024). SeqKit2: A Swiss army knife for sequence and alignment processing. iMeta, e191. [https://doi.org/10.1002/imt2.191](https://doi.org/10.1002/imt2.191)

## Software packaging/containerisation tools

- [Anaconda](https://anaconda.com)

  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

- [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)

  > Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature methods, 15(7), 475–476. [DOI: 10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)

- [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)

  > da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. [DOI: 10.1093/bioinformatics/btx192](https://doi.org/10.1093/bioinformatics/btx192)

- [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

- [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)

  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

GitHub Events

Total
  • Create event: 42
  • Release event: 1
  • Issues event: 37
  • Watch event: 16
  • Delete event: 38
  • Issue comment event: 185
  • Push event: 196
  • Pull request event: 80
  • Pull request review comment event: 227
  • Pull request review event: 173
  • Fork event: 10
Last Year
  • Create event: 42
  • Release event: 1
  • Issues event: 37
  • Watch event: 16
  • Delete event: 38
  • Issue comment event: 185
  • Push event: 196
  • Pull request event: 80
  • Pull request review comment event: 227
  • Pull request review event: 173
  • Fork event: 10

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 782
  • Total Committers: 14
  • Avg Commits per committer: 55.857
  • Development Distribution Score (DDS): 0.651
Past Year
  • Commits: 504
  • Committers: 13
  • Avg Commits per committer: 38.769
  • Development Distribution Score (DDS): 0.704
Top Committers
Name Email Commits
James Fellows Yates j****3@g****m 273
jasmezz j****g@h****e 169
Louisa Perelo l****o@q****e 99
Anan Ibrahim 8****6 81
darcy220606 a****o@h****m 65
Jasmin F 7****z 37
nf-core-bot c****e@n****e 27
louperelo 4****o 22
Moritz E. Beber m****r@p****t 4
darcy220606 A****m@a****e 1
Adam Talbot a****t@s****o 1
Robert Syme r****e@g****m 1
Harshil Patel d****h 1
louperelo l****o@g****m 1
Committer Domains (Top 20 + Academic)
seqera.io: 1 apate.hki-jena.de: 1 posteo.net: 1 nf-co.re: 1 qbic.uni-tuebingen.de: 1 hki-jena.de: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 99
  • Total pull requests: 189
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 9 days
  • Total issue authors: 28
  • Total pull request authors: 17
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.95
  • Merged pull requests: 144
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 24
  • Pull requests: 48
  • Average time to close issues: 7 days
  • Average time to close pull requests: 6 days
  • Issue authors: 10
  • Pull request authors: 10
  • Average comments per issue: 0.88
  • Average comments per pull request: 1.94
  • Merged pull requests: 30
  • Bot issues: 0
  • Bot pull requests: 0
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Pull Request Authors
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Top Labels
Issue Labels
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Pull Request Labels
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Dependencies

.github/workflows/awsfulltest.yml actions
  • actions/upload-artifact v3 composite
  • seqeralabs/action-tower-launch v2 composite
.github/workflows/awstest.yml actions
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modules/nf-core/abricate/run/meta.yml cpan
modules/nf-core/ampcombi/meta.yml cpan
modules/nf-core/ampir/meta.yml cpan
modules/nf-core/amplify/predict/meta.yml cpan
modules/nf-core/amrfinderplus/run/meta.yml cpan
modules/nf-core/amrfinderplus/update/meta.yml cpan
modules/nf-core/antismash/antismashlite/meta.yml cpan
modules/nf-core/antismash/antismashlitedownloaddatabases/meta.yml cpan
modules/nf-core/bakta/bakta/meta.yml cpan
modules/nf-core/bakta/baktadbdownload/meta.yml cpan
modules/nf-core/bioawk/meta.yml cpan
modules/nf-core/custom/dumpsoftwareversions/meta.yml cpan
modules/nf-core/deeparg/downloaddata/meta.yml cpan
modules/nf-core/deeparg/predict/meta.yml cpan
modules/nf-core/deepbgc/download/meta.yml cpan
modules/nf-core/deepbgc/pipeline/meta.yml cpan
modules/nf-core/fargene/meta.yml cpan
modules/nf-core/gecco/run/meta.yml cpan
modules/nf-core/gunzip/meta.yml cpan
modules/nf-core/hamronization/abricate/meta.yml cpan
modules/nf-core/hamronization/amrfinderplus/meta.yml cpan
modules/nf-core/hamronization/deeparg/meta.yml cpan
modules/nf-core/hamronization/fargene/meta.yml cpan
modules/nf-core/hamronization/rgi/meta.yml cpan
modules/nf-core/hamronization/summarize/meta.yml cpan
modules/nf-core/hmmer/hmmsearch/meta.yml cpan
modules/nf-core/macrel/contigs/meta.yml cpan
modules/nf-core/multiqc/meta.yml cpan
modules/nf-core/prodigal/meta.yml cpan
modules/nf-core/prokka/meta.yml cpan
modules/nf-core/pyrodigal/meta.yml cpan
modules/nf-core/rgi/main/meta.yml cpan
modules/nf-core/tabix/bgzip/meta.yml cpan
modules/nf-core/untar/meta.yml cpan
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