Open AccessBinary Classifier for Computing Posterior Error Probabilities in MetaMorpheus
Author(s) -
Michael R. Shortreed,
Robert J. Millikin,
Lei Liu,
Zach Rolfs,
Rachel Miller,
Leah V. Schaffer,
Brian L. Frey,
Lloyd M. Smith
Publication year - 2021
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.0c00838
Subject(s) - computer science , false discovery rate , binary number , classifier (uml) , data mining , algorithm , posterior probability , software , pattern recognition (psychology) , artificial intelligence , mathematics , chemistry , bayesian probability , biochemistry , arithmetic , gene , programming language
MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m / z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.