A symbolic regression based scoring system improving peptide identification for MS Amanda

Viktoria Dorfer, Sergey Maltsev, Stephan Dreiseitl, Karl Mechtler, Stephan M. Winkler

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

Peptide search engines are algorithms that are able to identify peptides (i.e., short proteins or parts of proteins) from mass spectra of biological samples. These identification algorithms report the best matching peptide for a given spectrum and a score that represents the quality of the match; usually, the higher this score, the higher is the reliability of the respective match. In order to estimate the specificity and sensitivity of search engines, sets of target sequences are given to the identification algorithm as well as so-called decoy sequences that are randomly created or scrambled versions of real sequences; decoy sequences should be assigned low scores whereas target sequences should be assigned high scores. In this paper we present an approach based on symbolic regression (using genetic programming) that helps to distinguish between target and decoy matches. On the basis of features calculated for matched sequences and using the information on the original sequence set (target or decoy) we learn mathematical models that calculate updated scores. As an alternative to this white box modeling approach we also use a black box modeling method, namely random forests. As we show in the empirical section of this paper, this approach leads to scores that increase the number of reliably identified samples that are originally scored using the MS Amanda identification algorithm for high resolution as well as for low resolution mass spectra.

OriginalspracheEnglisch
TitelGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
Redakteure/-innenSara Silva
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten1335-1341
Seitenumfang7
ISBN (elektronisch)9781450334884
ISBN (Print)9781450334884
DOIs
PublikationsstatusVeröffentlicht - 11 Juli 2015
Veranstaltung17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spanien
Dauer: 11 Juli 201515 Juli 2015

Publikationsreihe

NameGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Konferenz

Konferenz17th Genetic and Evolutionary Computation Conference, GECCO 2015
Land/GebietSpanien
OrtMadrid
Zeitraum11.07.201515.07.2015

Fingerprint

Untersuchen Sie die Forschungsthemen von „A symbolic regression based scoring system improving peptide identification for MS Amanda“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren