Cavalcante Raymond G, Patil Snehal, Weymouth Terry E, Bendinskas Kestutis G, Karnovsky Alla, Sartor Maureen A
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Chemistry, State University of New York at Oswego, Oswego, NY 13126, USA.
Bioinformatics. 2016 May 15;32(10):1536-43. doi: 10.1093/bioinformatics/btw016. Epub 2016 Jan 21.
Capabilities in the field of metabolomics have grown tremendously in recent years. Many existing resources contain the chemical properties and classifications of commonly identified metabolites. However, the annotation of small molecules (both endogenous and synthetic) to meaningful biological pathways and concepts still lags behind the analytical capabilities and the chemistry-based annotations. Furthermore, no tools are available to visually explore relationships and networks among functionally related groups of metabolites (biomedical concepts). Such a tool would provide the ability to establish testable hypotheses regarding links among metabolic pathways, cellular processes, phenotypes and diseases.
Here we present ConceptMetab, an interactive web-based tool for mapping and exploring the relationships among 16 069 biologically defined metabolite sets developed from Gene Ontology, KEGG and Medical Subject Headings, using both KEGG and PubChem compound identifiers, and based on statistical tests for association. We demonstrate the utility of ConceptMetab with multiple scenarios, showing it can be used to identify known and potentially novel relationships among metabolic pathways, cellular processes, phenotypes and diseases, and provides an intuitive interface for linking compounds to their molecular functions and higher level biological effects.
http://conceptmetab.med.umich.edu
akarnovsky@umich.edu or sartorma@umich.edu
Supplementary data are available at Bioinformatics online.
近年来,代谢组学领域的能力有了巨大的发展。许多现有资源包含了常见鉴定代谢物的化学性质和分类。然而,将小分子(内源性和合成性)注释到有意义的生物途径和概念仍然落后于分析能力和基于化学的注释。此外,没有工具可用于直观地探索功能相关代谢物组(生物医学概念)之间的关系和网络。这样的工具将提供建立关于代谢途径、细胞过程、表型和疾病之间联系的可测试假设的能力。
在此,我们展示了ConceptMetab,这是一个基于网络的交互式工具,用于绘制和探索由基因本体论、京都基因与基因组百科全书(KEGG)和医学主题词表开发的16069个生物学定义的代谢物集之间的关系,使用KEGG和PubChem化合物标识符,并基于关联统计测试。我们通过多种场景展示了ConceptMetab的实用性,表明它可用于识别代谢途径、细胞过程、表型和疾病之间已知和潜在的新关系,并提供了一个直观的界面,用于将化合物与其分子功能和更高层次的生物学效应联系起来。
http://conceptmetab.med.umich.edu
akarnovsky@umich.edu或sartorma@umich.edu
补充数据可在《生物信息学》在线获取。