Prioritization of putative metabolite identifications in LC-MS/MS experiments using a computational pipeline.
Prioritization of putative metabolite identifications in LC-MS/MS experiments using a computational pipeline.
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DOI：
10.1002/pmic.201200306
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发表时间：
2013-01
期刊：
影响因子：
3.4
通讯作者：
Ressom, Habtom W.
中科院分区：
文献类型：
--
作者：
Zhou, Bin;Xiao, Jun Feng;Ressom, Habtom W.
关键词：
当前基于 LC-MS 的代谢组学研究的主要瓶颈之一是代谢物鉴定。一种常用的方法是首先通过峰质量从数据库中查找代谢物，然后使用 MS/MS 数据验证所获得的假定鉴定。然而，当观察到的峰来自同位素、碎片或加合物时，基于质量的搜索可能会提供不适当的推定鉴定。此外，大部分峰通常会留下多个假定的标识。为了区分这些假定的鉴定，有必要通过生物样品和真实化合物之间的比较来手动验证代谢物。然而，这样的实验是费力的，特别是当遇到多个假定的识别时。在对代谢物进行实验验证之前，最好使用计算方法来获得更可靠的假定鉴定并对其进行优先排序。在本文中，提出了一种计算管道来协助代谢物识别，并提高代谢组覆盖率和优先排序能力。利用多个公开可用的软件工具和数据库以及内部开发的算法，充分利用从 LC-MS/MS 实验中获得的信息。该管道已成功应用于基于 LC-MS 和 MS/MS 数据识别代谢物。使用精确质量、保留时间值、MS/MS 谱图和代谢途径/网络，检索更合适的假定鉴定并确定优先级，以指导后续代谢物验证实验。
One of the major bottle-necks in current LC-MS based metabolomic investigations is metabolite identification. An often-used approach is to first look up metabolites from databases through peak mass, followed by verification of the obtained putative identifications using MS/MS data. However, the mass-based search may provide inappropriate putative identifications when the observed peak is from isotopes, fragments or adducts. In addition, a large fraction of peaks is often left with multiple putative identifications. To differentiate these putative identifications, manual verification of metabolites through comparison between biological samples and authentic compounds is necessary. However, such experiments are laborious especially when multiple putative identifications are encountered. It is desirable to use computational approaches to obtain more reliable putative identifications and prioritize them before performing experimental verification of the metabolites. In this paper, a computational pipeline is proposed to assist metabolite identification with improved metabolome coverage and prioritization capability. Multile publicly available software tools and databases, along with in-house developed algorithms, are utilized to fully exploit the information acquired from LC-MS/MS experiments.. The pipeline is successfully applied to identify metabolites on the basis of LC-MS as well as MS/MS data. Using accurate masses, retention time values, MS/MS spectra and metabolic pathways/networks, more appropriate putative identifications are retrieved and prioritized to guide subsequent metabolite verification experiments.
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影响因子：
14.9
作者：
Wishart DS;Knox C;Guo AC;Eisner R;Young N;Gautam B;Hau DD;Psychogios N;Dong E;Bouatra S;Mandal R;Sinelnikov I;Xia J;Jia L;Cruz JA;Lim E;Sobsey CA;Shrivastava S;Huang P;Liu P;Fang L;Peng J;Fradette R;Cheng D;Tzur D;Clements M;Lewis A;De Souza A;Zuniga A;Dawe M;Xiong Y;Clive D;Greiner R;Nazyrova A;Shaykhutdinov R;Li L;Vogel HJ;Forsythe I
通讯作者：
Forsythe I
影响因子：
3.7
作者：
Zhou B;Buckley ST;Patel V;Liu Y;Luo J;Krishnaveni MS;Ivan M;DeMaio L;Kim KJ;Ehrhardt C;Crandall ED;Borok Z
通讯作者：
Borok Z
影响因子：
3
作者：
Wolf S;Schmidt S;Müller-Hannemann M;Neumann S
通讯作者：
Neumann S
DOI：
10.1016/1044-0305(94)87009-8
发表时间：
1994-09-01
影响因子：
3.2
作者：
STEIN, SE;SCOTT, DR
通讯作者：
SCOTT, DR
影响因子：
5.8
作者：
Rogers, Simon;Scheltema, Richard A.;Breitling, Rainer
通讯作者：
Breitling, Rainer