MassWiz: A Novel Scoring Algorithm with Target-Decoy Based Analysis Pipeline for Tandem Mass Spectrometry
journal contributionposted on 06.05.2011, 00:00 by Amit Kumar Yadav, Dhirendra Kumar, Debasis Dash
Mass spectrometry has made rapid advances in the recent past and has become the preferred method for proteomics. Although many open source algorithms for peptide identification exist, such as X!Tandem and OMSSA, it has majorly been a domain of proprietary software. There is a need for better, freely available, and configurable algorithms that can help in identifying the correct peptides while keeping the false positives to a minimum. We have developed MassWiz, a novel empirical scoring function that gives appropriate weights to major ions, continuity of b-y ions, intensities, and the supporting neutral losses based on the instrument type. We tested MassWiz accuracy on 486,882 spectra from a standard mixture of 18 proteins generated on 6 different instruments downloaded from the Seattle Proteome Center public repository. We compared the MassWiz algorithm with Mascot, Sequest, OMSSA, and X!Tandem at 1% FDR. MassWiz outperformed all in the largest data set (AGILENT XCT) and was second only to Mascot in the other data sets. MassWiz showed good performance in the analysis of high confidence peptides, i.e., those identified by at least three algorithms. We also analyzed a yeast data set containing 106,133 spectra downloaded from the NCBI Peptidome repository and got similar results. The results demonstrate that MassWiz is an effective algorithm for high-confidence peptide identification without compromising on the number of assignments. MassWiz is open-source, versatile, and easily configurable.
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source algorithmsdata setsOMSSAinstruments downloadedAnalysis Pipelineinstrument typeSeattle Proteome CenterFDRconfigurable algorithms18 proteinsNovel Scoring AlgorithmNCBI Peptidome repositoryMassWiz accuracyyeast dataTandem Mass SpectrometryMass spectrometrypeptide identificationAGILENT XCTconfidence peptidesMassWiz algorithm