MacroSEQUEST: Efficient Candidate-Centric Searching and High-Resolution Correlation Analysis for Large-Scale Proteomics Data Sets

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.