Name
Title | ||
|---|---|---|
Peptide sequence tag-based blind identification of post-translational modifications with point process model. |
Abstract | ||
|---|---|---|
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.The software is available upon request. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1093/bioinformatics/btl226 | ISMB (Supplement of Bioinformatics) |
Keywords | Field | DocType |
blind identification,ab-initio sequence tag selection,yeast peptide database,peptide database,sequence tag,peptide sequence tag,post-translational modification,point process model,correct peptide,ptm identification,experimental spectrum,database search result,search space,score function,point process,spectrum,database search,deterministic finite automaton,post translational modification,tree decomposition,false positive rate,mass spectra | Data mining,Peptide sequence tag,Deterministic finite automaton,Computer science,Database search engine,Tree decomposition,Point process,Selection algorithm,Bioinformatics,Score,Peptide sequence | Conference |
Volume | Issue | ISSN |
22 | 14 | 1367-4811 |
Citations | PageRank | References |
7 | 0.83 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Mei Liu | 1 | 245 | 41.30 |
| Bo Yan | 2 | 49 | 7.88 |
| Ying-Lei Song | 3 | 140 | 19.21 |
| Ying Xu | 4 | 528 | 61.00 |
| Li-Ming Cai | 5 | 508 | 48.87 |