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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Novák, J. Hoksza, D. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Software Engineering, Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00, Czech Republic (Novák, J.; Hoksza, D.) |
| Abstract | Tandem mass spectrometry is a fast and modern method for determining protein and peptide sequences from an “in vitro” sample. A mass spectrometer outputs mass spectra, which are utilized for sequences identification. The successful methods for mass spectra interpretation are based on search in databases of already known or predicted protein sequences. Since the amount of protein sequences in the databases grows exponentially, couple of indexing approaches have been proposed to speed up the identification of the sequences corresponding to the mass spectra. However, many of these approaches do not (or poorly) support interpretation of the spectra contaminated with posttranslational modifications (PTMs), which in real-world conditions occur very often. We propose a promising method for dealing with PTMs in mass spectra, including efficient similarity search employing metric indexing. In this paper, we generalize a previously proposed method based on the parametrized Hausdorff distance (dHP), which can be used as a coarse filter for any other database-based mass spectra interpretation method. |
| Starting Page | 845 |
| Ending Page | 846 |
| File Size | 425034 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781424483037 |
| e-ISBN | 9781424483044 |
| DOI | 10.1109/BIBMW.2010.5703938 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Protein engineering Spectroscopy tandem mass spectrometry similarity search Peptides peptide identification Indexing |
| Content Type | Text |
| Resource Type | Article |
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