DMDtoolkit: a tool for visualizing the mutated dystrophin protein and predicting the clinical severity in DMD

BMC Bioinformatics

Software

Jing Xin¹  Yayun Niu¹  Shiwen Wu²  Jiapeng Zhou² 

[1] Department of Neurology, General Hospital of Chinese People’s Armed Police Forces, 100039, Beijing, China;Department of Neurology, General Hospital of Chinese People’s Armed Police Forces, 100039, Beijing, China;Precision Medical Laboratory, General Hospital of Chinese People’s Armed Police Forces, 100039, Beijing, China;

关键词: Duchenne muscular dystrophy (DMD);    Becker muscular dystrophy (BMD);    Assisted diagnosis;    Ambush hypothesis;    Hidden stop codons;   

DOI  :  10.1186/s12859-017-1504-4

 received in 2016-07-26, accepted in 2017-01-28,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Mutations in the dystrophin gene are responsible for both DMD and BMD. However, the clinical phenotypes and treatments are quite different in these two muscular dystrophies. Since early diagnosis and treatment results in better clinical outcome in DMD it is essential to establish accurate early diagnosis of DMD to allow efficient management. Previously, the reading-frame rule was used to predict DMD versus BMD. However, there are limitations using this traditional tool. Here, we report a novel molecular method to improve the accuracy of predicting clinical phenotypes in dystrophinopathy. We utilized several additional molecular genetic rules or patterns such as “ambush hypothesis”, “hidden stop codons” and “exonic splicing enhancer (ESE)” to predict the expressed clinical phenotypes as DMD versus BMD.ResultsA computer software “DMDtoolkit” was developed to visualize the structure and to predict the functional changes of mutated dystrophin protein. It also assists statistical prediction for clinical phenotypes. Using the DMDtoolkit we showed that the accuracy of predicting DMD versus BMD raised about 3% in all types of dystrophin mutations when compared with previous methods. We performed statistical analyses using correlation coefficients, regression coefficients, pedigree graphs, histograms, scatter plots with trend lines, and stem and leaf plots.ConclusionsWe present a novel DMDtoolkit, to improve the accuracy of clinical diagnosis for DMD/BMD. This computer program allows automatic and comprehensive identification of clinical risk and allowing them the benefit of early medication treatments. DMDtoolkit is implemented in Perl and R under the GNU license. This resource is freely available at http://github.com/zhoujp111/DMDtoolkit, and http://www.dmd-registry.com.

【 授权许可】

CC BY   
© The Author(s). 2017