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.
Highlights
Dystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD)
We developed a computer software DMDtoolkit, which was based on Perl (Practical extraction and reporting language) and R environment, to provide an aid to the diagnosis of DMD
Assisted diagnosis DMDtoolkit was used according to four rules: readingframe rule, length of potential protein, number of potential stop-gains, exonic splicing enhancer (ESE) rule, and several patterns on location of mutations
Summary
Dystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Duchenne muscular dystrophy (DMD) is an X-linked recessive disorder caused by dystrophin gene mutations [1]. It occurs in boys with an incidence rate of 1/3500 [2, 3]. The theory currently used to predict whether a mutation will result in a DMD or BMD phenotype is the readingframe rule (Monaco rule): “Adjacent exons that can maintain an open reading frame (ORF) in the spliced mRNA despite a deletion event would give rise to the less severe BMD phenotype and predict the production of a lower molecular weight, semifunctional dystrophin protein. Large deletions usually occur in the rod domain while large duplications mostly occur in the ABD domain
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