amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool | Zendy

Travis J. Lawrence | Zendy; Dana L. Carper | Zendy; Margaret K. Spangler | Zendy; Alyssa A. Carrell | Zendy; Tomás A. Rush | Zendy; Stephen J. Minter | Zendy; David J. Weston | Zendy; Jessy Labbé | Zendy

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amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

Author(s) -

Travis J. Lawrence,

Dana L. Carper,

Margaret K. Spangler,

Alyssa A. Carrell,

Tomás A. Rush,

Stephen J. Minter,

David J. Weston,

Jessy Labbé

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa917

Subject(s) - python (programming language) , computer science , random forest , classifier (uml) , open source , antimicrobial , antimicrobial peptides , data mining , machine learning , artificial intelligence , computational biology , software , programming language , biology , microbiology and biotechnology

Antimicrobial peptides (AMPs) are promising alternative antimicrobial agents. Currently, however, portable, user-friendly and efficient methods for predicting AMP sequences from genome-scale data are not readily available. Here we present amPEPpy, an open-source, multi-threaded command-line application for predicting AMP sequences using a random forest classifier.

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