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STEPS : A grid search methodology for optimized peptide identification filtering of MS / MS database search results
Author(s) -
Piehowski Paul D.,
Petyuk Vladislav A.,
Sandoval John D.,
Burnum Kristin E.,
Kiebel Gary R.,
Monroe Matthew E.,
Anderson Gordon A.,
Camp David G.,
Smith Richard D.
Publication year - 2013
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201200096
Subject(s) - database search engine , shotgun proteomics , computer science , identification (biology) , data mining , matching (statistics) , proteomics , set (abstract data type) , selection (genetic algorithm) , hyperparameter optimization , grid , database , search engine , information retrieval , machine learning , biology , mathematics , statistics , biochemistry , botany , geometry , support vector machine , gene , programming language
For bottom‐up proteomics, there are wide variety of database‐searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid‐search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection‐–referred to as STEPS ‐–utilizes user‐defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal “parameter set” for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true‐positive identifications are demonstrated using datasets derived from immunoaffinity‐depleted blood serum and a bacterial cell lysate, two common proteomics sample types.