High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics

被引：40

作者：

Colinge, J ^{[1
]}

Masselot, A ^{[1
]}

Cusin, I ^{[1
]}

Mahé, E ^{[1
]}

Niknejad, A ^{[1
]}

Argoud-Puy, G ^{[1
]}

Reffas, S ^{[1
]}

Bederr, N ^{[1
]}

Gleizes, A ^{[1
]}

Rey, PA ^{[1
]}

Bougueleret, L ^{[1
]}

机构：

[1] GeneProt Inc, CH-1217 Meyrin, Switzerland

来源：

PROTEOMICS | 2004年 / 4卷 / 07期

关键词：

algorithm; identification; model; spectrometry; tandem mass spectrometry;

D O I：

10.1002/pmic.200300708

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：1977 / 1984

页数：8

共 20 条

[11] Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search [J].