Journal Highlight: Scavager: A versatile postsearch validation algorithm for shotgun proteomics based on gradient boosting

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  • Published: Jan 23, 2019
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Scavager: A versatile postsearch validation algorithm for shotgun proteomics based on gradient boosting

A new postsearch validation tool for database protein identification called Scavager that used CatBoost, an open‐source gradient boosting library, showed improved efficiency compared with the other popular algorithms.

Ivanov, M.V., Levitsky, L.I., Bubis, J.A., and Gorshkov, M.V. (2018). Scavager: A versatile postsearch validation algorithm for shotgun proteomics based on gradient boosting. Proteomics 1800280 online http://dx.doi.org/10.1002/pmic.201800280

Abstract: Shotgun proteomics workflows for database protein identification typically include a combination of search engines and postsearch validation software based mostly on machine learning algorithms. Here, a new postsearch validation tool called Scavager employing CatBoost, an open‐source gradient boosting library, which shows improved efficiency compared with the other popular algorithms, such as Percolator, PeptideProphet, and Q‐ranker, is presented. The comparison is done using multiple data sets and search engines, including MSGF+, MSFragger, X!Tandem, Comet, and recently introduced IdentiPy. Implemented in Python programming language, Scavager is open‐source and freely available at https://bitbucket.org/markmipt/scavager.

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