| Title |
DMDtoolkit: a tool for visualizing the mutated dystrophin protein and predicting the clinical severity in DMD
|
|---|---|
| Published in |
BMC Bioinformatics, February 2017
|
| DOI | 10.1186/s12859-017-1504-4 |
| Pubmed ID | |
| Authors |
Jiapeng Zhou, Jing Xin, Yayun Niu, Shiwen Wu |
| Abstract |
Dystrophinopathy 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. A 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. We 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 . |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 22% |
| Researcher | 6 | 19% |
| Student > Bachelor | 5 | 16% |
| Student > Ph. D. Student | 5 | 16% |
| Student > Postgraduate | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 19% |
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Agricultural and Biological Sciences | 5 | 16% |
| Nursing and Health Professions | 2 | 6% |
| Veterinary Science and Veterinary Medicine | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 5 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 February 2017.
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#15,442,314
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Outputs from BMC Bioinformatics
#5,391
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Outputs of similar age from BMC Bioinformatics
#94
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