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 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.


CONCLUSIONS
msmsEval will be useful for high-throughput proteomics projects and is freely available for download from http://proteomics.ucd.ie/msmseval. Supports Windows, Mac OS X and Linux/Unix operating systems.
Wong
Jason W H
JW
Chemistry Department, Oxford University, Physical and Theoretical Chemistry Laboratory, South Parks Road, Oxford OX1 3QZ, UK. jason.wong@ucd.ie <jason.wong@ucd.ie>
Sullivan
Matthew J
MJ
Cartwright
Hugh M
HM
Cagney
Gerard
G
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000032:analysis* / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D020543:Proteome