MetaboAnalyst 3.0--making Metabolomics More Meaningful

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2015 Apr 22

PMID 25897128

Citations 1250

Authors

Jianguo Xia

Igor V Sinelnikov

BeomSoo Han

David S Wishart

Affiliations

Soon will be listed here.

Abstract

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.

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References

Bahado-Singh R, Akolekar R, Mandal R, Dong E, Xia J, Kruger M . Metabolomics and first-trimester prediction of early-onset preeclampsia. J Matern Fetal Neonatal Med. 2012; 25(10):1840-7. DOI: 10.3109/14767058.2012.680254. View

Amiour N, Imbaud S, Clement G, Agier N, Zivy M, Valot B . The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. J Exp Bot. 2012; 63(14):5017-33. DOI: 10.1093/jxb/ers186. View

Lin W, Hsueh H, Chen J . Power and sample size estimation in microarray studies. BMC Bioinformatics. 2010; 11:48. PMC: 2837028. DOI: 10.1186/1471-2105-11-48. View

van Iterson M, van de Wiel M, Boer J, de Menezes R . General power and sample size calculations for high-dimensional genomic data. Stat Appl Genet Mol Biol. 2013; 12(4):449-67. DOI: 10.1515/sagmb-2012-0046. View

Gieger C, Geistlinger L, Altmaier E, de Angelis M, Kronenberg F, Meitinger T . Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008; 4(11):e1000282. PMC: 2581785. DOI: 10.1371/journal.pgen.1000282. View

Zhang G, He P, Tan H, Budhu A, Gaedcke J, Ghadimi B . Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer. Clin Cancer Res. 2013; 19(18):4983-93. PMC: 3778077. DOI: 10.1158/1078-0432.CCR-13-0209. View

Psychogios N, Hau D, Peng J, Guo A, Mandal R, Bouatra S . The human serum metabolome. PLoS One. 2011; 6(2):e16957. PMC: 3040193. DOI: 10.1371/journal.pone.0016957. View

Nicholson J, Holmes E, Kinross J, Darzi A, Takats Z, Lindon J . Metabolic phenotyping in clinical and surgical environments. Nature. 2012; 491(7424):384-92. DOI: 10.1038/nature11708. View

Blydt-Hansen T, Sharma A, Gibson I, Mandal R, Wishart D . Urinary metabolomics for noninvasive detection of borderline and acute T cell-mediated rejection in children after kidney transplantation. Am J Transplant. 2014; 14(10):2339-49. DOI: 10.1111/ajt.12837. View

10.

Hailemariam D, Mandal R, Saleem F, Dunn S, Wishart D, Ametaj B . Identification of predictive biomarkers of disease state in transition dairy cows. J Dairy Sci. 2014; 97(5):2680-93. DOI: 10.3168/jds.2013-6803. View

11.

Xia J, Broadhurst D, Wilson M, Wishart D . Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013; 9(2):280-299. PMC: 3608878. DOI: 10.1007/s11306-012-0482-9. View

12.

Pluskal T, Castillo S, Villar-Briones A, Oresic M . MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010; 11:395. PMC: 2918584. DOI: 10.1186/1471-2105-11-395. View

13.

Wishart D, Jewison T, Guo A, Wilson M, Knox C, Liu Y . HMDB 3.0--The Human Metabolome Database in 2013. Nucleic Acids Res. 2012; 41(Database issue):D801-7. PMC: 3531200. DOI: 10.1093/nar/gks1065. View

14.

Xia J, Wishart D . MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics. 2010; 26(18):2342-4. DOI: 10.1093/bioinformatics/btq418. View

15.

Gowda H, Ivanisevic J, Johnson C, Kurczy M, Benton H, Rinehart D . Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem. 2014; 86(14):6931-9. PMC: 4215863. DOI: 10.1021/ac500734c. View

16.

Bertini I, Cacciatore S, Jensen B, Schou J, Johansen J, Kruhoffer M . Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Res. 2011; 72(1):356-64. DOI: 10.1158/0008-5472.CAN-11-1543. View

17.

Xia J, Mandal R, Sinelnikov I, Broadhurst D, Wishart D . MetaboAnalyst 2.0--a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012; 40(Web Server issue):W127-33. PMC: 3394314. DOI: 10.1093/nar/gks374. View

18.

Lin L, Huang Z, Gao Y, Yan X, Xing J, Hang W . LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery. J Proteome Res. 2010; 10(3):1396-405. DOI: 10.1021/pr101161u. View

19.

Xia J, Wishart D . MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 2010; 38(Web Server issue):W71-7. PMC: 2896187. DOI: 10.1093/nar/gkq329. View

20.

Tarraga J, Medina I, Carbonell J, Huerta-Cepas J, Minguez P, Alloza E . GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res. 2008; 36(Web Server issue):W308-14. PMC: 2447723. DOI: 10.1093/nar/gkn303. View