title: MacroSEQUEST: efficient candidate-centric searching and high-resolution correlation analysis for large-scale proteomics data sets.
abstract: Modern mass spectrometers are now capable of producing tens of thousands of tandem mass (MS/MS) spectra per hour of operation, resulting in an ever-increasing burden on the computational tools required to translate these raw MS/MS spectra into peptide sequences. In the present work, we describe our efforts to improve the performance of one of the earliest and most commonly used algorithms, SEQUEST, through a wholesale redesign of its processing architecture. We call this new program MacroSEQUEST, which exhibits a dramatic improvement in processing speed by transiently indexing the array of MS/MS spectra prior to searching FASTA databases. We demonstrate the performance of MacroSEQUEST relative to a suite of other programs commonly encountered in proteomics research. We also extend the capability of SEQUEST by implementing a parameter in MacroSEQUEST that allows for scalable sparse arrays of experimental and theoretical spectra to be implemented for high-resolution correlation analysis and demonstrate the advantages of high-resolution MS/MS searching to the sensitivity of large-scale proteomics data sets.
authors.affiliation: Department of Genetics, Dartmouth Medical School, Lebanon, New Hampshire 03756, USA.
authors.forename: Brendan K
authors.initials: BK
authors.lastname: Faherty
authors.forename: Scott A
authors.initials: SA
authors.lastname: Gerber
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D040901:Proteomics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods