Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Current Protocols in Bioinformatics - Tập 68 Số 1 - 2019
Jasmine Chong1, David S. Wishart2,3, Jianguo Xia4,5,1
1Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
2Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
3Department of Computing Science, University of Alberta. Edmonton, Alberta, Canada
4Department of Animal Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
5Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada

Tóm tắt

AbstractMetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.Basic Protocol 1: Data uploading, processing, and normalizationBasic Protocol 2: Identification of significant variablesBasic Protocol 3: Multivariate exploratory data analysisBasic Protocol 4: Functional interpretation of metabolomic dataBasic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curvesBasic Protocol 6: Time‐series and two‐factor data analysisBasic Protocol 7: Sample size estimation and power analysisBasic Protocol 8: Joint pathway analysisBasic Protocol 9: MS peaks to pathway activitiesBasic Protocol 10: Biomarker meta‐analysisBasic Protocol 11: Knowledge‐based network exploration of multi‐omics dataBasic Protocol 12: MetaboAnalystR introduction

Từ khóa


Tài liệu tham khảo

Ametaj B. N., 2010, Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows, Metabolomics, 5, 375

10.1021/ac051495j

10.1023/A:1010933404324

10.1186/1471-2105-11-376

10.1093/nar/gky310

10.1093/bioinformatics/bty528

10.3390/metabo9030057

10.1021/ac0519312

Deming S. N., 1986, Chemometrics: An overview, Clinical Chemistry, 32, 1702, 10.1093/clinchem/32.9.1702

10.1021/ac051632c

10.1101/gr.6202607

10.1198/016214501753382129

10.1214/07-AOAS101

10.1073/pnas.95.25.14863

Eisner R., 2010, Learning to predict cancer‐associated skeletal muscle wasting from 1H‐NMR profiles of urinary metabolites, Metabolomics, 3, 207

10.1007/s11306-010-0232-9

10.1158/1055-9965.EPI-15-0427

10.1023/A:1013713905833

10.1093/nar/gkp1002

10.1186/gb-2004-5-10-r80

10.1093/bioinformatics/btg382

10.1093/nar/gkm261

10.1186/1471-2105-10-11

10.1002/pmic.200700046

10.1038/nmeth.4260

10.1093/bioinformatics/btm531

10.1016/j.chom.2014.08.014

10.1093/biostatistics/kxj037

10.1093/bioinformatics/btt414

10.1021/ac202450g

10.1093/nar/gkm795

10.1371/journal.pcbi.1003123

10.1007/s11306-011-0369-1

10.1186/1748-7188-3-9

10.1038/srep16294

10.1186/gb-2001-2-10-research0042

10.1186/1471-2105-11-395

10.1021/pr070172w

10.1371/journal.pone.0124219

10.1038/nmeth.3959

10.1021/bi0480335

10.1021/ac2000994

10.1093/bioinformatics/bti476

10.1021/ac051437y

10.1093/bioinformatics/btm069

10.1073/pnas.0506580102

10.1214/009053606000000759

10.1021/ac300698c

10.1038/nmeth.3393

10.1073/pnas.091062498

10.1186/1471-2164-7-142

10.1186/1471-2164-10-439

10.1007/s11306-007-0099-6

Wickham H. &Chang W.(2016).Devtools: Tools to make developing r packages easier. R package version. 1(0). Available athttps://cran.r‐project.org/web/packages/devtools/index.html.

10.1007/s11306-012-0482-9

10.1093/nar/gks374

10.1093/nar/gkp356

10.1093/nar/gkv380

10.1093/bioinformatics/btr392

10.1093/nar/gkq329

10.1093/bioinformatics/btq418

10.1038/srep17201

Thông tin
Thông tin xuất bản

Current Protocols in Bioinformatics

Tập 68 Số 1

Thông tin tác giả