DocumentCode :
3409411
Title :
PPM-chain - de novo peptide identification program comparable in performance to Sequest
Author :
Day, Robert M. ; Borziak, Andrey ; Gorin, Andrey
Author_Institution :
Oak Ridge Nat. Lab., TN, USA
fYear :
2004
fDate :
16-19 Aug. 2004
Firstpage :
505
Lastpage :
508
Abstract :
Recently, we introduced probability profile method (PPM), which utilizes neutral-loss neighborhoods around each peak in MS/MS spectrum to "label" it: to assign a probability that the peak in question belongs to one of the specific categories (such as b- or y-ion peaks). Here we present the PPM-chain program - a PPM-based tool for de novo protein tag identification. De novo peptide identification involves finding a connected sequence of ion peaks separated by amino acid mass intervals, corresponding to a tag - partial peptide of the source protein. In the existing approaches the number of the possible connected sequences may run into hundreds of thousands, and it increases exponentially with the desired length of the tag. PPM can be used to locate high probability islands, containing very pure sets of b- and y-ion peaks, thereby reducing computational complexity and sharply increasing precision of tagging. In addition, the obtained tags can be reliably ranked using PPM-derived probabilities assigned to the connected peaks. The value of peptide tags was demonstrated on a large database of ∼20,000 spectra. With the additional flanking mass constraints PPM-chain shows precision and coverage similar to the field industrial-power standard - Sequest program, while providing a set of novel unique capabilities and significantly outperforming Sequest in speed.
Keywords :
biology computing; computational complexity; mass spectra; molecular biophysics; probability; proteins; Sequest program; amino acid; computational complexity; de novo peptide identification program; de novo protein tag identification; ion peaks; mass spectroscopy; neutral-loss neighborhoods; probability profile method-chain program; Amino acids; Computational biology; Computational complexity; Computer science; Databases; Laboratories; Mathematics; Peptides; Proteins; Sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE
Print_ISBN :
0-7695-2194-0
Type :
conf
DOI :
10.1109/CSB.2004.1332477
Filename :
1332477
Link To Document :
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