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Assessment of MS/MS Search Algorithms with Parent-Protein Profiling
Author(s) -
Miin S. Lin,
Justin J. Cherny,
Claire T. Fournier,
Samuel J. Roth,
Danny Kriz̧anc,
Michael P. Weir
Publication year - 2014
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/pr401090d
Subject(s) - algorithm , false discovery rate , decoy , computer science , mass spectrometry , data set , profiling (computer programming) , data mining , bioinformatics , chemistry , biology , chromatography , artificial intelligence , biochemistry , receptor , gene , operating system
Peptide mass spectrometry relies crucially on algorithms that match peptides to spectra. We describe a method to evaluate the accuracy of these algorithms based on the masses of parent proteins before trypsin endoprotease digestion. Measurement of conformance to parent proteins provides a score for comparison of the performances of different algorithms as well as alternative parameter settings for a given algorithm. Tracking of conformance scores for spectrum matches to proteins with progressively lower expression levels revealed that conformance scores are not uniform within data sets but are significantly lower for less abundant proteins. Similarly peptides with lower algorithm peptide-spectrum match scores have lower conformance. Although peptide mass spectrometry data is typically filtered through decoy analysis to ensure a low false discovery rate, this analysis confirms that the filtered data should not be considered as having a uniform confidence. The analysis suggests that use of different algorithms and multiple standardized parameter settings of these algorithms can increase significantly the numbers of peptides identified. This data set can be used as a resource for future algorithm assessment.

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