Editorial
Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Med Genet. Aug 27, 2015; 5(3): 46-51
Published online Aug 27, 2015. doi: 10.5496/wjmg.v5.i3.46
Value of predictive bioinformatics in inherited metabolic diseases
David J Timson
David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, BT9 7BL Belfast, United Kingdom
Author contributions: Timson DJ conceived and wrote the paper
Conflict-of-interest statement: The author has no conflicts of interest to declare.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, 97 Lisburn Road, BT9 7BL Belfast, United Kingdom. d.timson@qub.ac.uk
Telephone: +44-028-90975875 Fax: +44-028-90975877
Received: February 27, 2015
Peer-review started: March 2, 2015
First decision: April 27, 2015
Revised: April 28, 2015
Accepted: May 16, 2015
Article in press: May 18, 2015
Published online: August 27, 2015
Core Tip

Core tip: Bioinformatics and other in silico methods are increasingly being used to predict the severity of disease-associated mutations in inherited metabolic diseases. In general, severity correlates with altered protein stability and the best predictions occur when a variety of tools are applied.