Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.628438
Title: Development of tools for automated collection, integration and analysis of genetic data in ALS
Author: Abel, Olubunmi
ISNI:       0000 0004 5366 3799
Awarding Body: King's College London (University of London)
Current Institution: King's College London (University of London)
Date of Award: 2014
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Abstract:
Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, typically leads to death within 3-5 years of symptom onset. Understanding what causes ALS has been a challenge, but more research in this area, enhanced by advanced technology like high-throughput next generation sequencing, is paving the way for better information and direction. The volume of data generated by genetics researchers has dramatically increased, largely because of increased opportunities for collaboration. ALSoD, a widely used online genetics database for collating, analysing and integrating ALS data, has been updated with analytics tools and is able to portray the data graphically to users. Mutations and other gene variants have been mapped to genomic coordinates, and the inclusion of dbSNP ids has been implemented to facilitate the integration of data from numerous public sources. To increase the usability and functionality of ALSoD, population frequency of each variant found in the 1000 Genome Project and Exome Variation Server (EVS) databases is displayed. To contribute to a better understanding of the pathogenesis of ALS, links to information on animal models are also available. Furthermore, ALSoD can now be viewed on mobile devices and for Android platforms a mobile app is also available.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.628438  DOI: Not available
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