Repository: Freie Universit├Ąt Berlin, Math Department

EPIFANY: A Method for Efficient High-Confidence Protein Inference

Pfeuffer, Julianus and Sachsenberg, Timo and Dijkstra, Tjeerd M. H. and Serang, Oliver and Reinert, Knut and Kohlbacher, Oliver (2020) EPIFANY: A Method for Efficient High-Confidence Protein Inference. Journal of Proteome Research, 19 (3). pp. 1060-1072. ISSN 1535-3893

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Official URL: http://doi.org/10.1021/acs.jproteome.9b00566

Abstract

Accurate protein inference in the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets. Here, we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data, EPIFANY is the only tested method that finds all true-positive proteins at a 5% protein false discovery rate (FDR) without strict prefiltering on the peptide-spectrum match (PSM) level, yielding an increase in identification performance (+10% in the number of true positives and +14% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated. EPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

Item Type:Article
Uncontrolled Keywords:bottom-up proteomics, protein inference, Bayesian networks, convolution trees, loopy belief propagation, iPRG2016
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2429
Deposited By: Anja Kasseckert
Deposited On:02 Apr 2020 10:05
Last Modified:02 Apr 2020 10:05

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