TY - JOUR
T1 - HGCS
T2 - An online tool for prioritizing disease-causing gene variants by biological distance
AU - Itan, Yuval
AU - Mazel, Mark
AU - Mazel, Benjamin
AU - Abhyankar, Avinash
AU - Nitschke, Patrick
AU - Quintana-Murci, Lluis
AU - Boisson-Dupuis, Stephanie
AU - Boisson, Bertrand
AU - Abel, Laurent
AU - Zhang, Shen Ying
AU - Casanova, Jean Laurent
N1 - Funding Information:
We thank Aric Hagberg for advice about graph theory; George K. Lee, Gale Kremer and Joseph Alexander for helping with the webserver; Yelena Nemirovskaya, Eric Anderson, and Tiffany Nivare for administrative support; Jeanne Garbarino for student mentoring coordination; Michael Ciancanelli, Ruben Martinez Barricarte, Janet Markle and Fabien Lafaille for testing and discussion; and Yael Pinchevsky Itan for design advice and cover image illustration. YI was funded by an AXA Research Fund postdoctoral fellowship. This work was funded in part by the National Center for Research Resources and the National Center for Advancing Sciences (NCATS), National Institutes of Health (NIH) Grant 8 UL1 TR000043.
PY - 2014/4/3
Y1 - 2014/4/3
N2 - Background: 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.Results: 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/.Conclusions: The HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.
AB - Background: 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.Results: 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/.Conclusions: The HGCS should help investigators from diverse fields to identify new disease-causing candidate genes more effectively, via a user-friendly online interface.
UR - http://www.scopus.com/inward/record.url?scp=84899103596&partnerID=8YFLogxK
U2 - 10.1186/1471-2164-15-256
DO - 10.1186/1471-2164-15-256
M3 - Article
C2 - 24694260
AN - SCOPUS:84899103596
SN - 1471-2164
VL - 15
JO - BMC Genomics
JF - BMC Genomics
IS - 1
M1 - 256
ER -