AmPEPpy 1.0: A portable and accurate antimicrobial peptide prediction tool

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

doi:10.1093/bioinformatics/btaa917

AmPEPpy 1.0: A portable and accurate antimicrobial peptide prediction tool

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

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	2058-2060
Number of pages	3
Journal	Bioinformatics
Volume	37
Issue number	14
DOIs	https://doi.org/10.1093/bioinformatics/btaa917
State	Published - Jul 15 2021

Funding

D.L.C., A.A.C., T.A.R. and J.L.L. were supported by the Genomic Science Program, U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research as part of the Plant Microbe Interfaces Scientific Focus Area. T.J.L. and D.J.W. were supported by the Laboratory Directed Research and Development Program at Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725.

Access to Document

10.1093/bioinformatics/btaa917

Cite this

@article{c58b2cf8fb91431da5b4a7cebf1d85f6,

title = "AmPEPpy 1.0: A portable and accurate antimicrobial peptide prediction tool",

abstract = "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.",

author = "Lawrence, {Travis J.} and Carper, {Dana L.} and Spangler, {Margaret K.} and Carrell, {Alyssa A.} and Rush, {Tom{\'a}s A.} and Minter, {Stephen J.} and Weston, {David J.} and Labb{\'e}, {Jessy L.}",

note = "Publisher Copyright: {\textcopyright} 2020 Published by Oxford University Press 2020.",

year = "2021",

month = jul,

day = "15",

doi = "10.1093/bioinformatics/btaa917",

language = "English",

volume = "37",

pages = "2058--2060",

journal = "Bioinformatics",

issn = "1367-4803",

number = "14",

}

TY - JOUR

T1 - AmPEPpy 1.0

T2 - A portable and accurate antimicrobial peptide prediction tool

AU - Lawrence, Travis J.

AU - Carper, Dana L.

AU - Spangler, Margaret K.

AU - Carrell, Alyssa A.

AU - Rush, Tomás A.

AU - Minter, Stephen J.

AU - Weston, David J.

AU - Labbé, Jessy L.

PY - 2021/7/15

Y1 - 2021/7/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85113298027&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btaa917

DO - 10.1093/bioinformatics/btaa917

M3 - Article

C2 - 33135060

AN - SCOPUS:85113298027

SN - 1367-4803

VL - 37

SP - 2058

EP - 2060

JO - Bioinformatics

JF - Bioinformatics

IS - 14

ER -

AmPEPpy 1.0: A portable and accurate antimicrobial peptide prediction tool

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Cite this