msmsEval: tandem mass spectral quality assignment for high-throughput proteomics

Background: In proteomics experiments, database-search programs are the method of choice for protein identification from tandem mass spectra. As amino acid sequence databases grow however, computing resources required for these programs have become prohibitive, particularly in searches for modified proteins. Recently, methods to limit the number of spectra to be searched based on spectral quality have been proposed by different research groups, but rankings of spectral quality have thus far been based on arbitrary cut-off values. In this work, we develop a more readily interpretable spectral quality statistic by providing probability values for the likelihood that spectra will be identifiable. Results: We describe an application, msmsEval, that builds on previous work by statistically modeling the spectral quality discriminant function using a Gaussian mixture model. This allows a researcher to filter spectra based on the probability that a spectrum will ultimately be identified by da...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Jason W. H. Wong, Matthew J. Sullivan, Hugh M. Cartwright, Gerard Cagney
• Keywords: BMCBI 2007, Interpretable Spectral Quality, Spectral Quality, Tandem Mass Spectra