Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41643
Title: De novo peptide sequencing for mass spectra based on multi-charge strong tags
Authors: Ning, K.
Chong, K.F.
Leong, H.W. 
Issue Date: 2007
Source: Ning, K.,Chong, K.F.,Leong, H.W. (2007). De novo peptide sequencing for mass spectra based on multi-charge strong tags. Series on Advances in Bioinformatics and Computational Biology 5 : 287-296. ScholarBank@NUS Repository.
Abstract: This paper presents an improved algorithm for de novo sequencing of multi-charge mass spectra. Recent work based on the analysis of multi-charge mass spectra showed that taking advantage of multi-charge information can lead to higher accuracy (sensitivity and specificity) in peptide sequencing. A simple de novo algorithm, called GBST (Greedy algorithm with Best Strong Tag) was proposed and was shown to produce good results for spectra with charge > 2. In this paper, we analyze some of the shortcomings of GBST. We then present a new algorithm GST-SPC, by extending the GBST algorithm in two directions. First, we use a larger set of multi-charge strong tags and show that this improves the theoretical upper bound on performance. Second, we give an algorithm that computes a peptide sequence that is optimal with respect to shared peaks count from among all sequences that are derived from multi-charge strong tags. Experimental results demonstrate the improvement of GST-SPC over GBST.
Source Title: Series on Advances in Bioinformatics and Computational Biology
URI: http://scholarbank.nus.edu.sg/handle/10635/41643
ISBN: 9781860947834
ISSN: 17516404
Appears in Collections:Staff Publications

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