MetaBridge: enabling network-based integrative analysis via direct protein interactors of metabolites

Summary Here, we present MetaBridge, a tool that collates protein interactors (curated metabolite-enzyme interactions) that influence the levels of specific metabolites including both biosynthetic and degradative enzymes. This enables network-based integrative analysis of metabolomics data with other omics data types. MetaBridge is designed to complement a systems-biology approach to analysis, pairing well with network analysis tools such as NetworkAnalyst.ca, but can be used in any bioinformatics workflow. Availability and implementation MetaBridge has been implemented as a web tool at https://www.metabridge.org, and the source code is available at https://github.com/samhinshaw/metabridge_shiny (GNU GPLv3).

[1]  R. Goodacre,et al.  Metabolomics for the masses: The future of metabolomics in a personalized world , 2017, New horizons in translational medicine.

[2]  Peter D. Karp,et al.  Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology , 2016, Briefings Bioinform..

[3]  Peter D. Karp,et al.  The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases , 2007, Nucleic Acids Res..

[4]  G. Siuzdak,et al.  Innovation: Metabolomics: the apogee of the omics trilogy , 2012, Nature Reviews Molecular Cell Biology.

[5]  Weijun Luo,et al.  Pathview: an R/Bioconductor package for pathway-based data integration and visualization , 2013, Bioinform..

[6]  Advin K. Mathew METABOLOMICS: THE APOGEE OF THE OMICS TRILOGY , 2013 .

[7]  Robert E. W. Hancock,et al.  NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration , 2014, Nucleic Acids Res..

[8]  Peter D. Karp,et al.  Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology , 2015, Briefings Bioinform..

[9]  Kwanjeera Wanichthanarak,et al.  Genomic, Proteomic, and Metabolomic Data Integration Strategies , 2015, Biomarker insights.

[10]  Burkhard Morgenstern,et al.  Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets , 2014, PloS one.

[11]  Jacqueline A. Keane,et al.  Conditional-ready mouse embryonic stem cell derived macrophages enable the study of essential genes in macrophage function , 2015, Scientific Reports.

[12]  Bensu Karahalil,et al.  Overview of Systems Biology and Omics Technologies. , 2016, Current medicinal chemistry.

[13]  David S. Wishart,et al.  MetaboAnalyst 3.0—making metabolomics more meaningful , 2015, Nucleic Acids Res..

[14]  J. McPherson,et al.  Coming of age: ten years of next-generation sequencing technologies , 2016, Nature Reviews Genetics.

[15]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..