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Open Access Open Badges Methodology article

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

Jason WH Wong12, Matthew J Sullivan2, Hugh M Cartwright1 and Gerard Cagney2*

Author Affiliations

1 Chemistry Department, Oxford University, Physical and Theoretical Chemistry Laboratory, South Parks Road, Oxford OX1 3QZ, UK

2 Conway Institute, University College Dublin, Belfield, Dublin 4, Republic of Ireland

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BMC Bioinformatics 2007, 8:51  doi:10.1186/1471-2105-8-51

Published: 9 February 2007



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.


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 database searching. We show that spectra that are predicted by msmsEval to be of high quality, yet remain unidentified in standard database searches, are candidates for more intensive search strategies. Using a well studied public dataset we also show that a high proportion (83.9%) of the spectra predicted by msmsEval to be of high quality but that elude standard search strategies, are in fact interpretable.


msmsEval will be useful for high-throughput proteomics projects and is freely available for download from webcite. Supports Windows, Mac OS X and Linux/Unix operating systems.