Abstract
BackgroundIn 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.ResultsWe 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.ConclusionmsmsEval will be useful for high-throughput proteomics projects and is freely available for download from . Supports Windows, Mac OS X and Linux/Unix operating systems.
Highlights
In proteomics experiments, database-search programs are the method of choice for protein identification from tandem mass spectra
The introduction of orthogonal peptide separation techniques coupled to the mass spectrometer, such as multidimensional protein identification technology (MudPIT) [2] and combined fractional diagonal chromatography (COFRADIC) [3], has significantly increased the potential throughput of tandem mass spectrometry experiments, enabling the identification of 100s or 1000s of
We show that our assigned probability is a good estimate of the observed value and is of practical use in a proteomics lab using different instrument platforms or different types of experimental samples. msmsEval is useful for reducing search processing time and for selecting high quality unidentified spectra for further assessment
Summary
Database-search programs are the method of choice for protein identification from tandem mass spectra. BMC Bioinformatics 2007, 8:51 http://www.biomedcentral.com/1471-2105/8/51 proteins from a single sample This potential has not been fully realized because the vast amount of primary data generates computational burdens, notably time-consuming and processor-intensive tandem mass spectra interpretation. The most widely-used interpretation programs, such as SEQUEST [4], X!Tandem [5] and Mascot [6], use amino acid sequence databases that are expanding in size daily. Heuristic programs such as X!Tandem [5] and PFSM [7] have been reported to reduce search times by 80–90%. Search time would grow exponentially if the search space is increased to account for all possible modifications
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