Year: 2014
Type: article
Abstract: The human genome offers an attractive starting point for diabetes biomarker discovery. We have undertaken a survey of the Genetic Association Database (GAD) to develop a comprehensive genetic profiling of the type 1 and type 2 diabetes phenotypes. Using text mining, the GAD was explored for diabetes-associated genetic polymorphisms and a working database for type 1 and type 2 diabetes was established. In addition to well-characterized genes, 57 novel, uncharacterized Open Reading Frames (ORFs) encompassed in the dark matter of the human proteome were identified. Diversebioinformatics and proteomics tools were used to characterize these ORFs for geneexpression, protein motifs and domain information. Distinct protein classes including secreted products, enzymes, transporters, and receptors were encoded by these ORFs. Using expression Quantitative Traits Loci, Clinical Variations and the Genome-Phenome Integrator tools, 50 novel ORFs associated with phenotypes for both type 1 and type 2 diabetes were identified. These results open up new avenues for better understanding type 1 and type 2 diabetes and may provide novel therapy targets for type 2 diabetes and associated disorders.
Source: MOJ Proteomics & Bioinformatics
Author: Ramaswamy Narayanan
Institution: Florida Atlantic University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W2032058676