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An algorithm is introduced to assess spectral quality for peptide CID spectra acquired by a quadrupole ion trap mass spectrometer. The method employs a quadratic discriminant function calibrated with manually classified ‘bad' and ‘good' quality spectra, producing a single ‘spectral quality' score. Many spectra examined that do not have significant matches are assessed to have good spectral quality, indicating that advances in search methods may yield substantial improvements in results. Keywords: shotgun proteomics • mass spectral quality • peptide sequence • CID • quadrupole ion trap mass spectrometry

We describe a web-based program called ‘DBParser' for rapidly culling, merging, and comparing sequence search engine results from multiple LC−MS/MS peptide analyses. DBParser employs the principle of parsimony to consolidate redundant protein assignments and derive the most concise set of proteins consistent with all of the assigned peptide sequences observed in an experiment or series of experiments. The resulting reports summarize peptide and protein identifications from multidimensional experiments that may contain a single data set or combine data from a group of data sets, all related to a single analytical sample. Additionally, the results of multiple experiments, each of which may contain several data sets, can be compared in reports that identify features that are common or different. DBParser actively links to the primary mass spectral data and to public online databases such as NCBI, GO, and Swiss-Prot in order to structure contextually specific reports for biologists and biochemists. Keywords: proteomics • data analysis • peptides • mass spectrometry • software • bioinformatics

Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets. Keywords: protein identification • algorithm • bioinformatics • mass spectrometry • proteomics • significance testing