©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
Published online Aug 27, 2015. doi: 10.5496/wjmg.v5.i3.46
Value of predictive bioinformatics in inherited metabolic diseases
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.
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
Processing time: 179 Days and 13 Hours
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
Processing time: 179 Days and 13 Hours
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.