Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.
Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.
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DOI：
10.1007/s11306-009-0196-9
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发表时间：
2010-06
期刊：
影响因子：
3.6
通讯作者：
Debrauwer, Laurent
中科院分区：
文献类型：
--
作者：
Jourdan, Fabien;Cottret, Ludovic;Huc, Laurence;Wildridge, David;Scheltema, Richard;Hillenweck, Anne;Barrett, Michael P.;Zalko, Daniel;Watson, David G.;Debrauwer, Laurent
代谢组学实验很少达到全面覆盖整个代谢组的目的。然而，即使是从稀疏的数据集中也可以收集到重要的信息，这可以通过将结果放在已知代谢网络的上下文中来促进。在这里，我们提出了一种方法，允许自动分配已识别的代谢物的位置内已知的代谢网络，而且，允许自动提取的子网络的生物意义。后一个特征通过使用间隙填充算法是可能的。该算法在代谢组学数据的重建和挖掘中的实用性显示在两个独立的数据集上，所述数据集是用LC-MS LTQ-Orbitrap质谱法生成的。从两个数据集中提取生物相关的代谢子网络。此外，一些代谢物，其存在逃避质谱内的自动选择，可以追溯性地确定凭借其推断的存在，通过间隙填充。本文的在线版本（doi：10.1007/s11306-009-0196-9）包含补充材料，可供授权用户使用。
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC–MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.
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DOI：
10.1016/s0928-4869(00)00006-9
发表时间：
2000-04-15
期刊：
SIMULATION PRACTICE AND THEORY
影响因子：
--
作者：
Arita, M
通讯作者：
Arita, M
影响因子：
3
作者：
Chukualim, Bridget;Peters, Nick;Berriman, Matthew
通讯作者：
Berriman, Matthew
影响因子：
5.4
作者：
Antonov, Alexey V.;Dietmann, Sabine;Mewes, Hans W.
通讯作者：
Mewes, Hans W.
影响因子：
14.9
作者：
Paley, Suzanne M.;Karp, Peter D.
通讯作者：
Karp, Peter D.
影响因子：
14.9
作者：
Karp PD;Ouzounis CA;Moore-Kochlacs C;Goldovsky L;Kaipa P;Ahrén D;Tsoka S;Darzentas N;Kunin V;López-Bigas N
通讯作者：
López-Bigas N