CITATIONS.md

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

  CLi, 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., Kloosterman, A. M., Charlop-Powers, Z., van Wezel, G. P., Medema, M. H., & Weber, T. (2021). antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic acids research, 49(W1), W29–W35. DOI: 10.1093/nar/gkab335

• 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

• 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

• hAMRonization

  Public Health Alliance for Genomic Epidemiology (pha4ge). (2022). Parse multiple Antimicrobial Resistance Analysis Reports into a common data structure. Github. Retrieved October 5, 2022, from https://github.com/pha4ge/hAMRonization

• AMPcombi

  Anan Ibrahim, & Louisa Perelo. (2023). Darcy220606/AMPcombi. DOI: 10.5281/zenodo.7639121.

• HMMER

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

• 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

• 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

• RGI

  Alcock, B. P., Raphenya, A. R., Lau, T., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A. V., Cheng, A. A., Liu, S., Min, S. Y., Miroshnichenko, A., Tran, H. K., Werfalli, R. E., Nasir, J. A., Oloni, M., Speicher, D. J., Florescu, A., Singh, B., Faltyn, M., … McArthur, A. G. (2020). CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic acids research, 48(D1), D517–D525. DOI: 10.1093/nar/gkz935

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

• Singularity

  Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PloS one, 12(5), e0177459. DOI: 10.1371/journal.pone.0177459