Open AccessMutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases
Author(s) -
Atsushi Hijikata,
Rajesh Raju,
Shivakumar Keerthikumar,
Subhashri Ramabadran,
Lavanya Balakrishnan,
Suresh Kumar Ramadoss,
Akhilesh Pandey,
Sujatha Mohan,
Osamu Ohara
Publication year - 2010
Publication title -
dna research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 98
eISSN - 1756-1663
pISSN - 1340-2838
DOI - 10.1093/dnares/dsq010
Subject(s) - missense mutation , biology , mutation , genetics , computational biology , gene , genetic analysis
Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40,000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a 'one-stop' analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.