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HGCS: an online tool for prioritizing disease-causing gene variants by biological distance

Itan, Yuval ; Mazel, Mark ; Mazel, Benjamin ; Abhyankar, Avinash ; Nitschke, Patrick ; Quintana-Murci, Lluis ; Boisson-Dupuis, Stephanie ; Boisson, Bertrand ; Abel, Laurent ; Zhang, Shen-Ying ; Casanova, Jean-Laurent

BMC genomics, 03 April 2014, Vol.15, pp.256 [Tạp chí có phản biện]

E-ISSN: 1471-2164 ; PMID: 24694260 Version:1 ; DOI: 10.1186/1471-2164-15-256

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  • Nhan đề:
    HGCS: an online tool for prioritizing disease-causing gene variants by biological distance
  • Tác giả: Itan, Yuval ; Mazel, Mark ; Mazel, Benjamin ; Abhyankar, Avinash ; Nitschke, Patrick ; Quintana-Murci, Lluis ; Boisson-Dupuis, Stephanie ; Boisson, Bertrand ; Abel, Laurent ; Zhang, Shen-Ying ; Casanova, Jean-Laurent
  • Chủ đề: Databases, Genetic ; Software ; Web Browser ; Computational Biology -- Methods ; Genetic Association Studies -- Methods
  • Là 1 phần của: BMC genomics, 03 April 2014, Vol.15, pp.256
  • Mô tả: 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. We describe here the human gene connectome server (HGCS), a powerful, easy-to-use interactive online tool enabling researchers to prioritize any list of genes according to their biological proximity to core genes associated with the phenotype of interest. We also make available an updated and extended version for all human gene-specific connectomes. The HGCS is freely available to noncommercial users from: http://hgc.rockefeller.edu. The HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.
  • Ngôn ngữ: English
  • Số nhận dạng: E-ISSN: 1471-2164 ; PMID: 24694260 Version:1 ; DOI: 10.1186/1471-2164-15-256

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