Functional Interpretation of Omics Data by Profiling Genes and Diseases Using MeSH–Controlled Vocabulary

Abstract

One of the major aims of molecular biology and medical science is to understand disease mechanisms. A genetic disorder is a disease caused by abnormalities in genes and chromosomes, and researchers often report the identification of disease-relevant genes and correlations between phenotypes and genotypes (Butte & Kohane 2006; Lamb 2007; PerezIratxeta et al. 2002, 2005, 2007). Omics analysis using microarray, new generation sequencing (NGS) technology, and mass spectrometry is widely employed for determining genome sequences and profiling gene expression. Changes in gene expression on a genome-wide scale can be detected by omics analysis, which provides various types of huge datasets. These data are often archived in public databases; nucleotide sequences in the DDBJ/EMBL/GenBank International Nucleotide Sequence Database (INSD) (Cochrane et al. 2011), gene expression in Gene Expression Omnibus (GEO) (Barrett et al. 2011), and journal articles in MEDLINE. Currently, research cannot continue without the use of these databases. In Japan, the Database Center for Life Science (DBCLS) has developed infrastructure for researchers to access and easily reuse these data by providing index sites such as INSD and GEO yellow pages and by constructing a portal site for life science databases and tools. Researchers can easily analyze public data in conjunction with their own omics data. Here we present an analytical method to clarify the associations between genes and diseases. We characterized genes and diseases by assigning a MeSH-controlled vocabulary (Nakazato et al. 2008, 2009). Our objective was to help interpret omics data from molecular and clinical aspects by comparing these feature profiles.