[aut, cre],
Ira Cooke [aut],
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R)A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>. In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For details see Fingerhut et al. 2020 <doi:10.1101/2020.05.07.082412>.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Peptides, caret (≥ 6.0.0), kernlab, Rcpp, parallel |
| LinkingTo: | Rcpp |
| Suggests: | testthat, knitr, rmarkdown, e1071 |
| Published: | 2020-05-11 |
| Author: | Legana Fingerhut
[aut, cre],
Ira Cooke [aut],
Jinlong Zhang [ctb] (R/read_faa.R),
Nan Xiao [ctb] (R/calc_pseudo_comp.R) |
| Maintainer: | Legana Fingerhut <legana.fingerhut at my.jcu.edu.au> |
| License: | GPL-2 |
| URL: | https://github.com/Legana/ampir |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | ampir results |
| Reference manual: | ampir.pdf |
| Vignettes: |
Introduction to ampir How to train your model |
| Package source: | ampir_1.0.0.tar.gz |
| Windows binaries: | r-devel: ampir_1.0.0.zip, r-release: ampir_1.0.0.zip, r-oldrel: ampir_1.0.0.zip |
| macOS binaries: | r-release: ampir_1.0.0.tgz, r-oldrel: ampir_1.0.0.tgz |
| Old sources: | ampir archive |
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