Literature DB >> 20628077

MetPA: a web-based metabolomics tool for pathway analysis and visualization.

Jianguo Xia1, David S Wishart.   

Abstract

UNLABELLED: MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. AVAILABILITY: Freely available at http://metpa.metabolomics.ca.

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Mesh:

Year:  2010        PMID: 20628077     DOI: 10.1093/bioinformatics/btq418

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  234 in total

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4.  The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity.

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Authors:  Ravindra Taware; Khushman Taunk; Totakura V S Kumar; Jorge A M Pereira; José S Câmara; H A Nagarajaram; Gopal C Kundu; Srikanth Rapole
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6.  Metabolomics technology and bioinformatics for precision medicine.

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7.  Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst.

Authors:  Jianguo Xia; David S Wishart
Journal:  Nat Protoc       Date:  2011-05-05       Impact factor: 13.491

8.  Maternal hypercortisolemia alters placental metabolism: a multiomics view.

Authors:  Serene Joseph; Jacquelyn M Walejko; Sicong Zhang; Arthur S Edison; Maureen Keller-Wood
Journal:  Am J Physiol Endocrinol Metab       Date:  2020-09-21       Impact factor: 4.310

Review 9.  Opportunities and challenges for selected emerging technologies in cancer epidemiology: mitochondrial, epigenomic, metabolomic, and telomerase profiling.

Authors:  Mukesh Verma; Muin J Khoury; John P A Ioannidis
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2012-12-14       Impact factor: 4.254

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Authors:  Rance Nault; Kelly A Fader; Dustin A Ammendolia; Peter Dornbos; Dave Potter; Bonnie Sharratt; Kazuyoshi Kumagai; Jack R Harkema; Sophia Y Lunt; Jason Matthews; Tim Zacharewski
Journal:  Toxicol Sci       Date:  2016-08-25       Impact factor: 4.849

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