title: Efficient reduction of candidate matches in peptide spectrum library searching using the top k most intense peaks.
abstract: Spectral library searching is a popular approach for MS/MS-based peptide identification. Because the size of spectral libraries continues to grow, the performance of searching algorithms is an important issue. This technical note introduces a strategy based on a minimum shared peak count between two spectra to reduce the set of admissible candidate spectra when issuing a query. A theoretical validation through time complexity analysis and an experimental validation based on an implementation of the candidate reduction strategy show that the approach can achieve a reduction of the set of candidate spectra by (at least) an order of magnitude, resulting in a significant improvement in the speed of the search. Meanwhile, more than 99% of the positive search results is retained. This efficient strategy to drastically improve the speed of spectral library searching with a negligible loss of sensitivity can be applied to any current spectral library search tool, irrespective of the employed similarity metric.
authors.affiliation: Department of Mathematics and Computer Science, University of Antwerp , B-2020 Antwerp, Belgium.
authors.forename: Trung Nghia
authors.initials: TN
authors.lastname: Vu
authors.forename: Wout
authors.initials: W
authors.lastname: Bittremieux
authors.forename: Dirk
authors.initials: D
authors.lastname: Valkenborg
authors.forename: Bart
authors.initials: B
authors.lastname: Goethals
authors.forename: Filip
authors.initials: F
authors.lastname: Lemière
authors.forename: Kris
authors.initials: K
authors.lastname: Laukens
mesh_terms: D000465:Algorithms; D057225:Data Mining; D030562:Databases, Protein; D006801:Humans; D019151:Peptide Library; D011506:Proteins; D040901:Proteomics / Q000379:methods*; D012984:Software; D015003:Yeasts
chemical_list: D019151:Peptide Library; D011506:Proteins