Title |
Phenotype-driven strategies for exome prioritization of human Mendelian disease genes
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Published in |
Genome Medicine, July 2015
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DOI | 10.1186/s13073-015-0199-2 |
Pubmed ID | |
Authors |
Damian Smedley, Peter N. Robinson |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 31% |
Japan | 2 | 13% |
France | 2 | 13% |
Ecuador | 1 | 6% |
Spain | 1 | 6% |
Unknown | 5 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 10 | 63% |
Scientists | 5 | 31% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 2% |
Italy | 2 | <1% |
Brazil | 2 | <1% |
Korea, Republic of | 1 | <1% |
United States | 1 | <1% |
Unknown | 219 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 52 | 23% |
Student > Ph. D. Student | 49 | 21% |
Student > Master | 36 | 16% |
Other | 17 | 7% |
Student > Doctoral Student | 14 | 6% |
Other | 34 | 15% |
Unknown | 27 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 66 | 29% |
Agricultural and Biological Sciences | 54 | 24% |
Medicine and Dentistry | 31 | 14% |
Computer Science | 18 | 8% |
Neuroscience | 8 | 3% |
Other | 17 | 7% |
Unknown | 35 | 15% |