Bioinformatics Methods for Mass Spectra Analysis

质谱分析的生物信息学方法

• 批准号: 6988545
• 负责人: stephen h bryant
• 金额: --
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6988545
• 关键词: bioinformatics; mass spectrometry; mathematics; method development; molecular biology information system; protein quantitation /detection; protein sequence; statistics /biometry

My colleagues and I are developing methods for matching peptide mass spectra to entries in a protein sequence database. Our goals are to derive easily-interpreted tests of statistical significance, investigate the utility of restricting the search database by species of origin, and provide open source code that can be further tested and improved by other investigators. Work to date had focused on development of an objective measure of spectral noisiness and its relationship to success in protein identification using exisiting commercial matching algorithms. We have found, surprisingly, that spectral noise is a rather minor factor, with limitations in the matching algorithms being more often responsible for identification failure. We have also developed a preliminary version of OMSSA, an Open Mass Spectrometry Serch Algorithm. Tests have shown that the search hueristics used in OMSSA are as fast as those in commercial algorithms, and furthermore that OMSSA's significance test statistics offer somewhat improved signal-to-noise properties.

我和我的同事正在开发将肽质谱与蛋白质序列数据库中的条目相匹配的方法。我们的目标是得出统计显着性的易于解释的测试，研究按原点种类限制搜索数据库的实用性，并提供其他研究人员可以进一步测试和改进的开源代码。迄今为止的工作集中在开发光谱噪声的客观度量及其与使用Exisising商业匹配算法的蛋白质识别成功率的关系。令人惊讶的是，我们已经发现，光谱噪声是一个相当小的因素，匹配算法的局限性更常见于识别故障。我们还开发了OMSSA的初步版本，OMSSA是一种开放的质谱算法。测试表明，OMSSA中使用的搜索方法与商业算法中的搜索量一样快，此外，OMSSA的重要性测试统计数据提供了一些改善的信噪比。