Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40949
Title: A database search algorithm for identification of peptides with multiple charges using tandem mass spectrometry
Authors: Ning, K.
Chong, K.F.
Leong, H.W. 
Issue Date: 2006
Source: Ning, K.,Chong, K.F.,Leong, H.W. (2006). A database search algorithm for identification of peptides with multiple charges using tandem mass spectrometry. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3916 LNBI : 2-13. ScholarBank@NUS Repository.
Abstract: 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. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40949
ISBN: 3540331042
ISSN: 03029743
Appears in Collections:Staff Publications

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