Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39465
Title: Algorithm for peptide sequencing by tandem mass spectrometry based on better preprocessing and anti-symmetric computational model.
Authors: Ning, K.
Leong, H.W. 
Issue Date: 2007
Source: Ning, K.,Leong, H.W. (2007). Algorithm for peptide sequencing by tandem mass spectrometry based on better preprocessing and anti-symmetric computational model.. Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference 6 : 19-30. ScholarBank@NUS Repository.
Abstract: Peptide sequencing by tandem mass spectrometry is a very important, interesting, yet challenging problem in proteomics. This problem is extensively investigated by researchers recently, and the peptide sequencing results are becoming more and more accurate. However, many of these algorithms are using computational models based on some unverified assumptions. We believe that the investigation of the validity of these assumptions and related problems will lead to improvements in current algorithms. In this paper, we have first investigated peptide sequencing without preprocessing the spectrum, and we have shown that by introducing preprocessing on spectrum, peptide sequencing can be faster, easier and more accurate. We have then investigated one very important problem, the anti-symmetric problem in the peptide sequencing problem, and we have proved by experiments that model that simply ignore anti-symmetric of model that remove all anti-symmetric instances are too simple for peptide sequencing problem. We have proposed a new model for anti-symmetric problem in more realistic way. We have also proposed a novel algorithm which incorporate preprocessing and new model for anti-symmetric issue, and experiments show that this algorithm has better performance on datasets examined.
Source Title: Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/39465
ISSN: 17527791
Appears in Collections:Staff Publications

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