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- Abel, Olubunmi, et al.
(författare)
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Credibility analysis of putative disease-causing genes using bioinformatics
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:6, s. e64899-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective method for ranking the evidence is useful. Using the neurodegenerative and complex disease amyotrophic lateral sclerosis (ALS) as a model, and the disease-specific database ALSoD, the objective is to develop a method using publicly available data to generate a credibility score for putative disease-causing genes.Methods: Genes with at least one publication suggesting involvement in adult onset familial ALS were collated following an exhaustive literature search. SQL was used to generate a score by extracting information from the publications and combined with a pathogenicity analysis using bioinformatics tools. The resulting score allowed us to rank genes in order of credibility. To validate the method, we compared the objective ranking with a rank generated by ALS genetics experts. Spearman's Rho was used to compare rankings generated by the different methods.Results: The automated method ranked ALS genes in the following order: SOD1, TARDBP, FUS, ANG, SPG11, NEFH, OPTN, ALS2, SETX, FIG4, VAPB, DCTN1, TAF15, VCP, DAO. This compared very well to the ranking of ALS genetics experts, with Spearman's Rho of 0.69 (P = 0.009).Conclusion: We have presented an automated method for scoring the level of evidence for a gene being disease-causing. In developing the method we have used the model disease ALS, but it could equally be applied to any disease in which there is genotypic uncertainty.
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| 2. |
- Abel, Olubunmi, et al.
(författare)
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Development of a Smartphone App for a Genetics Website : The Amyotrophic Lateral Sclerosis Online Genetics Database (ALSoD)
- 2013
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Ingår i: JMIR mhealth and uhealth. - : JMIR Publications, Inc.. - 2291-5222. ; 1:2
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Tidskriftsartikel (refereegranskat)abstract
- Background: The ALS Online Genetics Database (ALSoD) website holds mutation, geographical, and phenotype data on genes implicated in amyotrophic lateral sclerosis (ALS) and links to bioinformatics resources, publications, and tools for analysis. On average, there are 300 unique visits per day, suggesting a high demand from the research community. To enable wider access, we developed a mobile-friendly version of the website and a smartphone app. Objective: We sought to compare data traffic before and after implementation of a mobile version of the website to assess utility. Methods: We identified the most frequently viewed pages using Google Analytics and our in-house analytic monitoring. For these, we optimized the content layout of the screen, reduced image sizes, and summarized available information. We used the Microsoft. NET framework mobile detection property (HttpRequest. IsMobileDevice in the Request. Browser object in conjunction with HttpRequest. UserAgent), which returns a true value if the browser is a recognized mobile device. For app development, we used the Eclipse integrated development environment with Android plug-ins. We wrapped the mobile website version with the WebView object in Android. Simulators were downloaded to test and debug the applications. Results: The website automatically detects access from a mobile phone and redirects pages to fit the smaller screen. Because the amount of data stored on ALSoD is very large, the available information for display using smartphone access is deliberately restricted to improve usability. Visits to the website increased from 2231 to 2820, yielding a 26% increase from the pre-mobile to post-mobile period and an increase from 103 to 340 visits (230%) using mobile devices (including tablets). The smartphone app is currently available on BlackBerry and Android devices and will be available shortly on iOS as well. Conclusions: Further development of the ALSoD website has allowed access through smartphones and tablets, either through the website or directly through a mobile app, making genetic data stored on the database readily accessible to researchers and patients across multiple devices.
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