ArticleBMC bioinformatics2017
DMDtoolkit: a tool for visualizing the mutated dystrophin protein and predicting the clinical severity in DMD.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 10 citations in OpenAlex.
- Antisense oligonucleotides and their applications in rare neurological diseases.Frontiers in neuroscience · 2024Review
- Genetic Profile of the Dystrophin Gene Reveals New Mutations in Colombian Patients Affected with Muscular Dystrophinopathy.The application of clinical genetics · 2021Article
- [A predictive analysis of the association between clinical phenotypes and genotypes in children with Becker muscular dystrophy/Duchenne muscular dystrophy].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2020Article
- Muscular Dystrophy: A Retrospective Evaluation of 15 Cases.Sisli Etfal Hastanesi tip bulteni · 2018Article
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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 .
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