Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be es...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2009-03, Vol.9 (5), p.1220-1229
Main Authors: Jones, Andrew R, Siepen, Jennifer A, Hubbard, Simon J, Paton, Norman W
Format: Article
Language:English
Subjects:
Algorithms
Analytical, structural and metabolic biochemistry
Biological and medical sciences
Computational Biology - methods
Databases, Protein
Decoy database
False discovery rate
Fundamental and applied biological sciences. Psychology
Information Storage and Retrieval
MASCOT
Miscellaneous
Models, Statistical
OMSSA
Peptides - analysis
Proteins
Proteins - analysis
Proteomics - methods
Reproducibility of Results
Software
X!Tandem
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Description
Summary:LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.200800473