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  "Title": "Predict Antimicrobial Peptides",
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  "Date": "2021-06-29",
  "Authors@R": "c(person(\"Legana\",\"Fingerhut\",\nemail = \"legana.fingerhut@my.jcu.edu.au\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-2482-5336\")),\nperson(\"Ira\",\"Cooke\",\nemail = \"ira.cooke@jcu.edu.au\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-6520-1397\")),\nperson(\"Jinlong\",\"Zhang\",\nemail = \"jinlongzhang01@gmail.com\",\nrole = c(\"ctb\"),\ncomment = c(\"R/read_faa.R\")),\nperson(\"Nan\",\"Xiao\",\nemail = \"me@nanx.me\",\nrole = c(\"ctb\"),\ncomment = c(\"R/calc_pseudo_comp.R\")))",
  "Description": "A toolkit to predict antimicrobial peptides from protein\nsequences on a genome-wide scale. It incorporates two support\nvector machine models (\"precursor\" and \"mature\") trained on\npublicly available antimicrobial peptide data using calculated\nphysico-chemical and compositional sequence properties\ndescribed in Meher et al. (2017) <doi:10.1038/srep42362>. In\norder to support genome-wide analyses, these models are\ndesigned to accept any type of protein as input and calculation\nof compositional properties has been optimised for\nhigh-throughput use. For best results it is important to select\nthe model that accurately represents your sequence type: for\nfull length proteins, it is recommended to use the default\n\"precursor\" model. The alternative, \"mature\", model is best\nsuited for mature peptide sequences that represent the final\nantimicrobial peptide sequence after post-translational\nprocessing. For details see Fingerhut et al. (2020)\n<doi:10.1093/bioinformatics/btaa653>. The 'ampir' package is\nalso available via a Shiny based GUI at\n<https://ampir.marine-omics.net/>.",
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