EFFICIENT MARGINALIZATION TO COMPUTE PROTEIN POSTERIOR PROBABILITIES FROM SHOTGU

通过 Shotgu 进行有效边缘化计算蛋白质后验概率

• 批准号: 8365888
• 负责人: William Noble
• 金额: $ 2.14万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8365888
• 关键词: Address; Algorithms; Biology; Data Set; Funding; Fungal Genome; Grant; Graph; Maps; Methods; National Center for Research Resources; Peptides; Principal Investigator; Probability; Procedures; Proteins; Proteomics; Research; Research Infrastructure; Resources; Sampling; Set protein; Shotguns; Source; Statistical Models; United States National Institutes of Health; cost; heuristics; research study; tandem mass spectrometry

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, ?degenerate? peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein?s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or estimated the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors.

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