Journal Article

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Atsushi Hijikata, Rajesh Raju, Shivakumar Keerthikumar, Subhashri Ramabadran, Lavanya Balakrishnan, Suresh Kumar Ramadoss, Akhilesh Pandey, Sujatha Mohan and Osamu Ohara

in DNA Research

Published on behalf of Kazusa DNA Research Institute

Volume 17, issue 3, pages 197-208
Published in print June 2010 | ISSN: 1340-2838
Published online April 2010 | e-ISSN: 1756-1663 | DOI: http://dx.doi.org/10.1093/dnares/dsq010

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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.

Keywords: genetic disease; mutation; polymorphism; bioinformatics; protein structure

Journal Article.  5140 words.  Illustrated.

Subjects: Genetics and Genomics

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