Computational Core

计算核心-项目摘要 化合物鉴定，特别是基于LC-MS的代谢组学，大多被视为一个挑战 这必须凭经验来解决。一种替代完全实验性的识别策略是， 利用计算方法来帮助解释MS和MS/MS数据。计算 碰撞诱导解离（CID）类型下代谢物碎裂的解释方法 条件正在改善，这既是因为计算机模拟MS/MS文库的生成取得了进展，也是因为在文库方面， 搜索策略，可以更好地应对较难预测的性质和较低的信息内容， 小分子片段化。在鉴定现有和未知化合物方面取得进展， 未来的代谢组学数据集，并提高通量和降低未来化合物鉴定的成本 在作出这些努力的同时，必须将这些工具与实验方法结合起来使用。第二个挑战有助于 非靶向代谢组学数据中未识别特征的高比例是冗余的 电喷雾电离质谱数据中的（简并）特征，包括同位素、源内 片段和加合物。现有的工具不足以应对碎片的复杂性 和加合物，预测和未知的，已被证明发生在电喷雾电离 质谱分析法来一个更有用的工具也将有助于分析师辅助解释特征冗余， 这是MCIDC执行的仔细系统的化合物ID工作流程的重要一步。 与MCIDC的行政和实验核心以及共同体协调运作 基金代谢组学联盟，MCIDC计算核心将有助于解决在 非靶向代谢组学领域通过执行以下具体目标：我们将开发和应用一种新的 软件工具Binner，以减少非目标代谢组学数据中特征的简并性。有效利用 Binner将允许我们优先考虑主要特征的识别工作，同时允许退化特征， 在代谢物光谱数据库中按此编入索引，并更快地从今后的数据集中删除。接下来， 我们将实施一种新的概率串联质谱搜索小分子代谢物的策略， 包括“混合搜索”方法。我们的方法将允许检测常见的结构基序， 未知代谢物，并有助于确定其身份。与实验核心合作，我们将 使用来自生物数据的已知代谢物的光谱来验证和改进该评分算法。

Computational Core - Project Summary Compound identification, particularly for LC-MS based metabolomics, has mostly been viewed as a challenge which must be tackled empirically. An alternative to exclusively experimental identification strategies is to leverage computational approaches to aid in interpretation of MS and MS/MS data. Computational approaches to interpretation of metabolite fragmentation under collision-induced dissociation (CID) type conditions are improving, both because of progress generating in-silico MS/MS libraries, and in terms of library search strategies which can better contend with the less-predictable nature and lower information content of small-molecule fragmentation. To make headway on identification of unknown compounds in existing and future metabolomics data sets, and to improve throughput and reduce cost of future compound identification efforts, it is essential to use these tools alongside experimental approaches. A second challenge contributing to the high proportion of unidentified features in untargeted metabolomics data is the abundance of redundant (degenerate) features in electrospray ionization mass spectrometry data, which include isotopes, in-source fragments and adducts. Presently existing tools are insufficient to contend with the complexity of fragments and adducts, both predicted and unknown, that have been demonstrated to occur in electrospray ionization mass spectrometry. A more useful tool would also facilitate analyst-aided interpretation of feature redundancy, an important step for the careful systematic compound ID workflow to be performed by MCIDC. Operating in coordination with the Administrative and Experimental Cores of MCIDC and with the Common Funds Metabolomics Consortium, the MCIDC Computational Core will help address major challenges in the field of untargeted metabolomics by carrying out the following Specific Aims: We will develop and apply a novel software tool, Binner, to reduce degeneracy of features in untargeted metabolomics data. Effective use of Binner will allow us to prioritize identification efforts on primary features, while allowing degenerate features to be indexed as such in metabolite spectral databases and be more rapidly removed from future data sets. Next, we will implement a novel probabilistic tandem mass spectral search strategy for small-molecule metabolites, including a “Hybrid Search” approach. Our approach will allow detection of common structural motifs in unknown metabolites and aid in determination of their identity. Working with the experimental core, we will validate and refine this scoring algorithm using spectra of known metabolites from biological data.