Organization of GC/MS and LC/MS metabolomics data into chemical libraries.
Organization of GC/MS and LC/MS metabolomics data into chemical libraries.
复制标题
DOI：
10.1186/1758-2946-2-9
复制
发表时间：
2010-10-18
影响因子：
8.6
通讯作者：
Lawton KA
中科院分区：
文献类型：
--
作者：
Dehaven CD;Evans AM;Dai H;Lawton KA
代谢组学实验涉及从复杂的混合物样品中生成和比较小分子（代谢物）概况，以识别那些在改变状态（例如疾病、药物治疗、毒素暴露）下受到调节的代谢物。一种非靶向代谢组学方法尝试使用 GC 或 LC 分离，然后进行 MS 或 MSn 检测来识别和询问样品中的所有小分子。对由此产生的大型多方面数据集进行分析以快速准确地识别代谢物是一项具有挑战性的任务，它依赖于代谢物光谱特征化学库的可用性。一种用于分析光谱数据以识别和量化样品中各个成分的方法 (QUICS)，能够根据已知标准生成化学库条目，更重要的是，根据实验样品中存在但没有相应库条目的未知代谢物生成化学库条目。该方法考虑了样品光谱中的所有离子，执行库匹配，并允许审查数据以质量检查库条目。 QUICS 方法通过关联整套实验样品中的离子数据来识别与任何给定代谢物相关的离子，从而揭示微妙的光谱趋势，这些趋势在查看单个样品时可能并不明显，但很可能表明存在一种或多种原本模糊的代谢物。同时分析了 33 个肝脏样品的 LC-MS/MS 或 GC-MS 数据，利用了样品固有的生物多样性以及在多个样品中观察时代谢物的很大程度上非协变的化学性质。离子按保留时间 (RT) 和协方差进行划分，将来自单一常见基础代谢物的离子分组。这种方法受益于在整个样品集中使用质量、时间和强度数据来拒绝异常值和噪音，从而产生更高质量的化学特性。将聚合的数据与参考化学库进行匹配，以帮助将离子集识别为已知的代谢物或添加到库中的新的未知生化物质。 QUICS 方法能够快速、深入地评估一组样品中所有可能的代谢物（已知和未知），以识别代谢物，并且对于那些在参考库中没有条目的代谢物，创建一个库条目以在未来的研究中识别该代谢物。
Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
登录
查看更多内容
影响因子：
64.8
作者：
Sreekumar, Arun;Poisson, Laila M.;Rajendiran, Thekkelnaycke M.;Khan, Amjad P.;Cao, Qi;Yu, Jindan;Laxman, Bharathi;Mehra, Rohit;Lonigro, Robert J.;Li, Yong;Nyati, Mukesh K.;Ahsan, Aarif;Kalyana-Sundaram, Shanker;Han, Bo;Cao, Xuhong;Byun, Jaeman;Omenn, Gilbert S.;Ghosh, Debashis;Pennathur, Subramaniam;Alexander, Danny C.;Berger, Alvin;Shuster, Jeffrey R.;Wei, John T.;Varambally, Sooryanarayana;Beecher, Christopher;Chinnaiyan, Arul M.
通讯作者：
Chinnaiyan, Arul M.
影响因子：
2.9
作者：
Tolstikov, VV;Fiehn, O
通讯作者：
Fiehn, O
影响因子：
3.6
作者：
Boudonck, Kurt J.;Mitchell, Matthew W.;Ryals, John A.
通讯作者：
Ryals, John A.
影响因子：
5.3
作者：
Watson, Mark;Roulston, Anne;Beauparlant, Pierre
通讯作者：
Beauparlant, Pierre
影响因子：
7.4
作者：
Alves, Alexessander Couto;Rantalainen, Mattias;Ebbels, Timothy M. D.
通讯作者：
Ebbels, Timothy M. D.