High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics
In a previous paper we introduced a novel model‐based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1–2% false positive rate at 95...
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Published in | Proteomics (Weinheim) Vol. 4; no. 7; pp. 1977 - 1984 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Weinheim
WILEY-VCH Verlag
01.07.2004
WILEY‐VCH Verlag Wiley-VCH |
Subjects | |
Online Access | Get full text |
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Summary: | In a previous paper we introduced a novel model‐based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1–2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects. |
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Bibliography: | ark:/67375/WNG-HPFLQRRB-3 istex:57C94F95F302922DA43A15B007A8BC52214B551D ArticleID:PMIC200300708 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1615-9853 1615-9861 |
DOI: | 10.1002/pmic.200300708 |