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nf-core/funcscan: Citations

nf-core

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

Nextflow

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

Pipeline tools

  • ABRicate

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

  • AMPir

    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

  • AMPlify

    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

  • AMRFinderPlus

    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

  • AntiSMASH

    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

  • argNorm

    Ugarcina Perovic, S., Ramji, V., Chong, H., Duan, Y., Maguire, F., Coelho, L. P. (2025). argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO), Bioinformatics, btaf173. DOI: 10.1093/bioinformatics/btaf173

  • Bakta

    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

  • comBGC

    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

  • DeepARG

    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

  • DeepBGC

    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

  • fARGene

    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

  • GECCO

    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

  • AMPcombi

    Ibrahim, A. & Perelo, L. (2023). paleobiotechnology/AMPcombi. DOI: 10.5281/zenodo.7639121.

  • 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

  • HMMER

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

  • InterPro

    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

  • InterProScan

    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

  • Macrel

    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

  • MMseqs2

    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

  • Prodigal

    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

  • PROKKA

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

  • Pyrodigal

    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

  • RGI

    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

  • dbCAN

    Jinfang Zheng, Qiwei Ge, Yuchen Yan, Xinpeng Zhang, Le Huang, Yanbin Yin, dbCAN3: automated carbohydrate-active enzyme and substrate annotation, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W115–W121. DOI:10.1093/nar/gkad328

  • 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

Software packaging/containerisation tools

  • Anaconda

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

  • Bioconda

    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

  • BioContainers

    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

  • Docker

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

  • Singularity

    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.