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Bioinformatics 2006 22(14):e307-e313; doi:10.1093/bioinformatics/btl226
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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Peptide sequence tag-based blind identification of post-translational modifications with point process model

Chunmei Liu 1,*, Bo Yan 2, Yinglei Song 1, Ying Xu 2 and Liming Cai 1,*

1 Department of Computer Science, University of Georgia Athens, GA 30602
2 Department of Biochemistry and Molecular Biology, University of Georgia Athens, GA 30602

*To whom correspondence should be addressed.

An important but difficult problem in proteomics is the identification of post-translational modifications (PTMs) in a protein. In general, the process of PTM identification by aligning experimental spectra with theoretical spectra from peptides in a peptide database is very time consuming and may lead to high false positive rate. In this paper, we introduce a new approach that is both efficient and effective for blind PTM identification. Our work consists of the following phases. First, we develop a novel tree decomposition based algorithm that can efficiently generate peptide sequence tags (PSTs) from an extended spectrum graph. Sequence tags are selected from all maximum weighted antisymmetric paths in the graph and their reliabilities are evaluated with a score function. An efficient deterministic finite automaton (DFA) based model is then developed to search a peptide database for candidate peptides by using the generated sequence tags. Finally, a point process model—an efficient blind search approach for PTM identification, is applied to report the correct peptide and PTMs if there are any. Our tests on 2657 experimental tandem mass spectra and 2620 experimental spectra with one artificially added PTM show that, in addition to high efficiency, our ab-initio sequence tag selection algorithm achieves better or comparable accuracy to other approaches. Database search results show that the sequence tags of lengths 3 and 4 filter out more than 98.3% and 99.8% peptides respectively when applied to a yeast peptide database. With the dramatically reduced search space, the point process model achieves significant improvement in accuracy as well.

Availability: The software is available upon request.

Contact: {chunmei,cai}@cs.uga.edu


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