COLANDER: A PROBABILITY-BASED SUPPORT VECTOR MACHINE ALGORITHM FOR AUTOMATIC SCR

COLANDER：一种基于概率的自动 SCR 支持向量机算法

• 批准号: 7957740
• 负责人: JOHN L YATES
• 金额: $ 2.09万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957740
• 关键词: Algorithms; Biology; Cells; Computer Retrieval of Information on Scientific Projects Database; Data Set; Databases; Diagnostic; Funding; Fungal Genome; Grant; Institution; Ions; Machine Learning; Mass Spectrum Analysis; Phosphopeptides; Probability; Proteolysis; Research; Research Personnel; Resources; Source; Time; Tissues; Training; United States National Institutes of Health; base; programs; research study; statistics

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We developed a probability-based machine-learning program, Colander, to identify tandem mass spectra that are highly likely to represent phosphopeptides prior to database search. We identified statistically significant diagnostic features of phosphopeptide tandem mass spectra based on ion trap CID MS/MS experiments. Statistics for the features are calculated from 376 validated phosphopeptide spectra and 376 nonphosphopeptide spectra. A probability-based support vector machine (SVM) program, Colander, was then trained on five selected features. Data sets were assembled both from LC/LC-MS/MS analyses of large-scale phosphopeptide enrichments from proteolyzed cells, tissues and synthetic phosphopeptides. These data sets were used to evaluate the capability of Colander to select pS/pT-containing phosphopeptide tandem mass spectra. When applied to unknown tandem mass spectra, Colander can routinely remove 80% of tandem mass spectra while retaining 95% of phosphopeptide tandem mass spectra. The program significantly reduced computational time spent on database search by 60-90%. Furthermore, prefiltering tandem mass spectra representing phosphopeptides can increase the number of phosphopeptide identifications under a predefined false positive rate.

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 我们开发了一个基于概率的机器学习程序，Colander，以确定串联质谱，很可能代表磷酸肽数据库搜索之前。基于离子阱CID MS/MS实验，我们确定了磷酸肽串联质谱的统计学显著诊断特征。从376个经验证的磷酸肽光谱和376个非磷酸肽光谱计算特征的统计。一个基于概率的支持向量机（SVM）程序，Colander，然后训练五个选定的功能。从LC/LC-MS/MS分析蛋白水解细胞、组织和合成磷酸肽的大规模磷酸肽富集物中收集数据集。这些数据集用于评价Colander选择含pS/pT磷酸肽串联质谱的能力。当应用于未知的串联质谱时，Colander可以常规地去除80%的串联质谱，同时保留95%的磷酸肽串联质谱。该程序将数据库搜索的计算时间显著减少了60- 90%。此外，代表磷酸肽的预过滤串联质谱可以在预定义的假阳性率下增加磷酸肽鉴定的数量。