Obesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10-25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development.
Pubmed ID: 27907186 RIS Download
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Multi species reference database. Comprehensive plant biochemical pathway database, containing curated information from literature and computational analyses about genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism.
View all literature mentionsWeb tool to search, sort, analyze, visualize and download data of interest. Along with providing details of the ontologies, gene products and annotations, features a BLAST search, Term Enrichment and GO Slimmer tools, the GO Online SQL Environment and a user help guide.Used at the Gene Ontology (GO) website to access the data provided by the GO Consortium. Developed and maintained by the GO Consortium.
View all literature mentionsComputable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
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