Title |
HGCS: an online tool for prioritizing disease-causing gene variants by biological distance
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Published in |
BMC Genomics, April 2014
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DOI | 10.1186/1471-2164-15-256 |
Pubmed ID | |
Authors |
Yuval Itan, Mark Mazel, Benjamin Mazel, Avinash Abhyankar, Patrick Nitschke, Lluis Quintana-Murci, Stephanie Boisson-Dupuis, Bertrand Boisson, Laurent Abel, Shen-Ying Zhang, Jean-Laurent Casanova |
Abstract |
Identifying the genotypes underlying human disease phenotypes is a fundamental step in human genetics and medicine. High-throughput genomic technologies provide thousands of genetic variants per individual. The causal genes of a specific phenotype are usually expected to be functionally close to each other. According to this hypothesis, candidate genes are picked from high-throughput data on the basis of their biological proximity to core genes - genes already known to be responsible for the phenotype. There is currently no effective gene-centric online interface for this purpose. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 14% |
United States | 1 | 14% |
Italy | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
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Scientists | 4 | 57% |
Members of the public | 2 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Spain | 1 | 1% |
United States | 1 | 1% |
Slovenia | 1 | 1% |
Unknown | 75 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 17 | 22% |
Researcher | 16 | 21% |
Student > Bachelor | 11 | 14% |
Student > Ph. D. Student | 10 | 13% |
Other | 5 | 6% |
Other | 8 | 10% |
Unknown | 11 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 23% |
Biochemistry, Genetics and Molecular Biology | 17 | 22% |
Medicine and Dentistry | 12 | 15% |
Computer Science | 9 | 12% |
Immunology and Microbiology | 3 | 4% |
Other | 5 | 6% |
Unknown | 14 | 18% |