Systems biology guided by XCMS Online metabolomics.
Systems biology guided by XCMS Online metabolomics.
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DOI：
10.1038/nmeth.4260
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发表时间：
2017-04-27
期刊：
影响因子：
48
通讯作者：
Siuzdak G
中科院分区：
文献类型：
--
作者：
Huan T;Forsberg EM;Rinehart D;Johnson CH;Ivanisevic J;Benton HP;Fang M;Aisporna A;Hilmers B;Poole FL;Thorgersen MP;Adams MWW;Krantz G;Fields MW;Robbins PD;Niedernhofer LJ;Ideker T;Majumder EL;Wall JD;Rattray NJW;Goodacre R;Lairson LL;Siuzdak G
编者按：系统生物学的一个目标是通过集成和建模多个数据源来了解基因、蛋白质和代谢物之间的复杂相互作用。我们在XCMS Online 1中报告了一种‘集成组学’方法，它自动将原始代谢组数据叠加到代谢途径上，并将其与转录组和蛋白质组数据(http://XCMSOnline.)相结合斯克里普斯。将下游代谢物的变化映射到代谢途径和生物网络上可以提供相当大的机械性洞察力，这可以通过与多组数据的关联来证实。然而，使用非靶向代谢组学的途径分析需要密集的数据管理，包括特征过滤、统计分析和代谢物识别。需要主观定义的值，如折叠变化、P值和信号强度截止值，以在海量数据集中识别显著失调的代谢物特征。为了确认代谢产物的同一性进行通路分析，通常需要进行额外的串联质谱学(MS/MS)实验，并将光谱与标准或MS/MS光谱数据库进行匹配。这些数据集的规模使得手动解释是不切实际的，因此在每个步骤中使用生物信息学工具是必不可少的。通常需要多个分析平台来完成整个工作流程，这可能需要几周时间，具体取决于样本队列的大小和分析师的经验。
To the Editor: An aim of systems biology is to understand complex interactions between genes, proteins and metabolites by integrating and modeling multiple data sources. We report an ‘integrated-omics’ approach within XCMS Online1 that automatically superimposes raw metabolomic data onto metabolic pathways and integrates it with transcriptomic and proteomic data (http://XCMSOnline. scripps. edu/).Mapping downstream metabolite changes onto metabolic pathways and biological networks can provide considerable mechanistic insight that can be confirmed by association to multi-omic data. However, pathway analysis using untargeted metabolomics requires intense data curation, including feature filtering, statistical analysis and metabolite identification. Subjectively defined values such as fold change, P value and signal intensity cut-off are needed to identify significantly dysregulated metabolite features within enormous data sets. Confirming metabolite identities for pathway analysis typically requires performing additional tandem mass spectrometry (MS/MS) experiments and matching the spectra to standards or MS/MS spectral databases. The magnitude of these data sets makes it impractical to manually interpret, and therefore the use of bioinformatic tools at each step is essential. Multiple analysis platforms are often needed to complete the entire workflow, which can take several weeks, depending on the size of the sample cohort and the experience of the analyst.
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影响因子：
7.4
作者：
Gowda, Harsha;Ivanisevic, Julijana;Johnson, Caroline H.;Kurczy, Michael E.;Benton, H. Paul;Rinehart, Duane;Thomas Nguyen;Ray, Jayashree;Kuehl, Jennifer;Arevalo, Bernardo;Westenskow, Peter D.;Wang, Junhua;Arkin, Adam P.;Deutschbauer, Adam M.;Patti, Gary J.;Siuzdak, Gary
通讯作者：
Siuzdak, Gary
影响因子：
9.2
作者：
Davidson RL;Weber RJ;Liu H;Sharma-Oates A;Viant MR
通讯作者：
Viant MR
影响因子：
7.4
作者：
Benton, H. Paul;Ivanisevic, Julijana;Mahieu, Nathaniel G.;Kurczy, Michael E.;Johnson, Caroline H.;Franco, Lauren;Rinehart, Duane;Valentine, Elizabeth;Gowda, Harsha;Ubhi, Baljit K.;Tautenhahn, Ralf;Gieschen, Andrew;Fields, Matthew W.;Patti, Gary J.;Siuzdak, Gary
通讯作者：
Siuzdak, Gary
影响因子：
4.3
作者：
Li S;Park Y;Duraisingham S;Strobel FH;Khan N;Soltow QA;Jones DP;Pulendran B
通讯作者：
Pulendran B
影响因子：
3.4
作者：
Aiche, Stephan;Sachsenberg, Timo;Kenar, Erhan;Walzer, Mathias;Wiswedel, Bernd;Kristl, Theresa;Boyles, Matthew;Duschl, Albert;Huber, Christian G.;Berthold, Michael R.;Reinert, Knut;Kohlbacher, Oliver
通讯作者：
Kohlbacher, Oliver