Bioinformatic screening of autoimmune disease genes and protein structure prediction with FAMS for drug discovery.

ABSTRACT: Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease.

SUBMITTER: Ishida S 

PROVIDER: S-EPMC4141326 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature