ALGORITHMS FOR AUTOMATIC PROCESSING OF DATA FROM MASS SPECTROMETRIC ANALYSES

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 最近发展起来的脂类分析的质谱学方法，特别是电喷雾电离(ESI)串联质谱仪，可以从少量的生物样品中识别和测量大量不同的脂类分子物种，但在短时间内产生大量的实验数据。处理这样的数据集，以便从中获得可理解的信息，需要生物信息学工具，而为蛋白质组学和基因组学开发的算法提供了一些可以直接适应脂肪组学的策略。然而，脂类的结构多样性和复杂性也要求开发和应用专门针对处理脂类分析数据的新算法和软件工具。

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. Recently developed mass spectrometric methods for lipid analyses, especially electrospray ionization (ESI) tandem mass spectrometry, permit identification and measurement of an enormous variety of distinct lipid molecular species from small amounts of biological samples but generate a huge amount of experimental data within a brief interval. Processing such data sets so that comprehensible information is derived from them requires bioinformatics tools, and algorithms developed for proteomics and genomics have provided some strategies that can be directly adapted to lipidomics. The structural diversity and complexity of lipids, however, also requires the development and application of new algorithms and software tools that are specifically directed at processing data from lipid analyses.