Publication Name	World Journal of Medical Genetics
Manuscript ID	17298
Country	United Kingdom

Received	2015-02-28 10:04
Peer-Review Started	2015-03-02 19:04
To Make the First Decision	2015-04-27 15:40
Return for Revision	2015-04-28 15:02
Revised	2015-04-28 17:44
Second Decision	2015-05-07 09:56
Accepted by Journal Editor-in-Chief	2015-05-07 18:00
Accepted by Executive Editor-in-Chief	2015-05-18 17:04
Articles in Press	2015-05-18 17:04
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2015-08-19 09:08
Publish the Manuscript Online	2015-08-26 12:03

ISSN	2220-3184 (online)
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/
Copyright	© The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com

Category	Genetics & Heredity
Manuscript Type	Editorial
Article Title	Value of predictive bioinformatics in inherited metabolic diseases
Manuscript Source	Invited Manuscript
All Author List	David J Timson
Funding Agency and Grant Number
Corresponding Author	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
Key Words	Genetic disease; Metabolism; In silico method; Protein stability; Disease-associated mutation
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.
Publish Date	2015-08-26 12:03
Citation	Timson DJ. Value of predictive bioinformatics in inherited metabolic diseases. World J Med Genet 2015; 5(3): 46-51
URL	http://www.wjgnet.com/2220-3184/full/v5/i3/46.htm
DOI	http://dx.doi.org/10.5496/wjmg.v5.i3.46

Full Article (PDF)	WJMG-5-46.pdf
Full Article (Word)	WJMG-5-46.doc
Manuscript File	17298-Review.docx
Answering Reviewers	17298-Answering reviewers.pdf
Audio Core Tip	17298-Audio core tip.wav
Conflict-of-Interest Disclosure Form	17298-Conflict-of-interest statement.pdf
Copyright License Agreement	17298-Copyright assignment.pdf
Peer-review Report	17298-Peer-review(s).pdf
Journal Editor-in-Chief Review Report	17298-Journal editor-in-chief review report.pdf
Scientific Misconduct Check	17298-Scientific misconduct check.pdf
Scientific Editor Work List	17298-Scientific editor work list.pdf