Scavager: A Versatile Postsearch Validation Algorithm for Shotgun Proteomics Based on Gradient Boosting.
Proteomics
; 19(3): e1800280, 2019 02.
Article
in En
| MEDLINE
| ID: mdl-30537264
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Proteomics
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Proteomics
Journal subject:
BIOQUIMICA
Year:
2019
Type:
Article
Affiliation country:
RUSSIA