![]() The fundamental algorithmic advantage of the latter approach is that one can preprocess the database ( e.g. A faster approach would be to generate a full-length de novo reconstruction of a spectrum and to match the resulting peptide against a database. Most peptide identification tools are rather slow because they match every tandem mass (MS/MS) 1 spectrum against all peptides in a database (subject to constraints on the precursor mass, the enzyme specificity, and the number of missed cleavages). We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches. MS-GappedDictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. Our MS-GappedDictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification.
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