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dc.contributor.authorSmedley, Damian
dc.contributor.authorRobinson, Peter N
dc.date.accessioned2015-09-01T17:43:10Z
dc.date.available2015-09-01T17:43:10Z
dc.date.issued2015-07-30
dc.identifier.citationGenome Medicine. 2015 Jul 30;7(1):81
dc.identifier.urihttp://dx.doi.org/10.1186/s13073-015-0199-2
dc.identifier.urihttp://hdl.handle.net/10724/31857
dc.description.abstractAbstract Whole exome sequencing has altered the way in which rare diseases are diagnosed and disease genes identified. Hundreds of novel disease-associated genes have been characterized by whole exome sequencing in the past five years, yet the identification of disease-causing mutations is often challenging because of the large number of rare variants that are being revealed. Gene prioritization aims to rank the most probable candidate genes towards the top of a list of potentially pathogenic variants. A promising new approach involves the computational comparison of the phenotypic abnormalities of the individual being investigated with those previously associated with human diseases or genetically modified model organisms. In this review, we compare and contrast the strengths and weaknesses of current phenotype-driven computational algorithms, including Phevor, Phen-Gen, eXtasy and two algorithms developed by our groups called PhenIX and Exomiser. Computational phenotype analysis can substantially improve the performance of exome analysis pipelines.
dc.titlePhenotype-driven strategies for exome prioritization of human Mendelian disease genes
dc.typeJournal Article
dc.date.updated2015-07-29T18:44:49Z
dc.language.rfc3066en
dc.rights.holderSmedley and Robinson.


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