Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Mutation-tolerant protein identification by mass-spectrometry
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
SPIDER: Software for Protein Identification from Sequence Tags with De Novo Sequencing Error
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Classifying b and y ions in peptide tandem mass spectra
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
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Peptide sequencing using tandem mass spectrometry is the process of interpreting the peptide sequence from a given mass spectrum. Peptide sequencing is an important but challenging problem in bioinformatics. The advancement in mass spectrometry machines has yielded great amount of high quality spectra data, but the methods to analyze these spectra to get peptide sequences are still accurate. There are two types of peptide sequencing methods –database search methods and the de novo methods. Much progress has been made, but the accuracy and efficiency of these methods are not satisfactory and improvements are urgently needed. In this paper, we will introduce a database search algorithm for sequencing of peptides using tandem mass spectrometry. This Peptide Sequence Pattern (PSP) algorithm first generates the peptide sequence patterns (PSPs) by connecting the strong tags with mass differences. Then a linear time database search process is used to search for candidate peptide sequences by PSPs, and the candidate peptide sequences are then scored by share peaks count. The PSP algorithm is designed for peptide sequencing from spectra with multiple charges, but it is also applicable for singly charged spectra. Experiments have shown that our algorithm can obtain better sequencing results than current database search algorithms for many multiply charged spectra, and comparative results for singly charged spectra against other algorithms.