| abstract |
A new algorithm, SHERENGA, for de novo spectral interpretation is described that automatically learns fragment ion-types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The algorithm employs a graph theory approach. The test data is used to construct optimal path scoring in the graph representations of tandem mass spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins not yet encountered in genome sequencing, and leveraging text based pattern matching for homology matching to known proteins. The algorithm also serves as a powerful adjunct for validating the results of database matching algorithms in fully automated, high-throughput peptide sequencing. |