Abstract
Peptide identification by tandem mass spectrometry is both an important and challenging problem in proteomics. At present, huge amount of spectrum data are generated by high throughput mass spectrometers at a very fast pace, but algorithms to analyze these spectra are either too slow, not accurate enough, or only gives partial sequences or sequence tags. In this paper, we emphasize on the balance between identification completeness and efficiency with reasonable accuracy for peptide identification by tandem mass spectrum. Our method works by converting spectra to vectors in high-dimensional space, and subsequently use self-organizing map (SOM) and multi-point range query (MPRQ) algorithm as a coarse filter reduce the number of candidates to achieve efficient and accurate database search. Experiments show that our algorithm is both fast and accurate in peptide identification.
