Combining Results of Multiple Search Engines in Proteomics

• Published in: Molecular & cellular proteomics Vol. 12; no. 9; pp. 2383 - 2393
• Main Authors: Shteynberg, David, Nesvizhskii, Alexey I., Moritz, Robert L., Deutsch, Eric W.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-09-2013; The American Society for Biochemistry and Molecular Biology
• Subjects: Amino Acid Sequence; Animals; Humans; Peptides - metabolism; Proteomics - methods; Review; ROC Curve; Search Engine; Software
• ISSN: 1535-9476; 1535-9484
• DOI: 10.1074/mcp.R113.027797

A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques.