A Novel Algorithm for Validating Peptide Identification from a Shotgun Proteomics Search Engine
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- 作者：Ling Jian ; Xinnan Niu ; Zhonghang Xia ; Parimal Samir ; Chiranthani Sumanasekera ; Zheng Mu ; Jennifer L. Jennings ; Kristen L. Hoek ; Tara Allos ; Leigh M. Howard ; Kathryn M. Edwards ; P. Anthony Weil ; Andrew J. Link
- 刊名：Journal of Proteome Research
- 出版年：2013
- 出版时间：March 1, 2013
- 年：2013
- 卷：12
- 期：3
- 页码：1108-1119
- 全文大小：546K
- 年卷期：v.12,no.3(March 1, 2013)
- ISSN：1535-3907
文摘
Liquid chromatography coupled with tandem mass spectrometry (LC鈥揗S/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC鈥揗S/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC鈥揗S/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.