Computational Elucidation of the REgulatory NonKOding Variome
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Computational Elucidation of the REgulatory NonKOding Variome

CERENKOV is a software pipeline and associated machine-learning framework for identifying regulatory single nucleotide polymorphisms (rSNPs) in the noncoding genome for post-analysis of genetic regions identified in genome-wide association studies (GWAS). CERENKOV was created by Yao Yao, Zheng Liu, Satpreet Singh, Qi Wei, and Stephen Ramsey at Oregon State University.

The March 2017 data files for CERENKOV can be accessed on the Ramsey Lab file server (see files under the subdirectories of the GitHub CERENKOV project area, for more information about which data files are used in which parts of CERENKOV).

News Update: CERENKOV paper accepted at ACM-BCB conference

We will be presenting CERENKOV at the 2017 ACM-BCB conference in Boston in August 2017 (with an accompanying full research article in the proceedings, describing CERENKOV and demonstrating its accuracy for discriminating rSNPs from nonfunctional SNPs). PDF will be posted here soon!