NDLI: CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome

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CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome

Content Provider	ACM Digital Library
Author	Ramsey, Stephen A. Yao, Yao Wei, Qi Liu, Zheng Singh, Satpreet
Abstract	We describe a novel computational approach, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome), for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within noncoding genetic loci. CERENKOV is specifically designed for recognizing rSNPs in the context of a post-analysis of a genome-wide association study (GWAS); it includes a novel accuracy scoring metric (which we call average rank, or AVGRANK) and a novel cross-validation strategy (locus-based sampling) that both correctly account for the "sparse positive bag" nature of the GWAS post-analysis rSNP recognition problem. We trained and validated CERENKOV using a reference set of 15,331 SNPs (the OSU17 SNP set) whose composition is based on selection criteria (linkage disequilibrium and minor allele frequency) that we designed to ensure relevance to GWAS post-analysis. CERENKOV is based on a machine-learning algorithm (gradient boosted decision trees) incorporating 246 SNP annotation features that we extracted from genomic, epigenomic, phylogenetic, and chromatin datasets. CERENKOV includes novel features based on replication timing and DNA shape. We found that tuning a classifier for AUPVR performance does not guarantee optimality for AVGRANK. We compared the validation performance of CERENKOV to nine other methods for rSNP recognition (including GWAVA, RSVP, DeltaSVM, DeepSEA, Eigen, and DANQ), and found that CERENKOV's validation performance is the strongest out of all of the classifiers that we tested, by both traditional global rank-based measures (〈AUPVR〉 = 0.506; 〈AUROC〉 = 0.855) and AVGRANK (〈AVGRANK〉 = 3.877). The source code for CERENKOV is available on GitHub and the SNP feature data files are available for download via the CERENKOV website.
Starting Page	79
Ending Page	88
Page Count	10
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107414
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Gwas Noncoding Rsnp Snp Machine learning Snv
Content Type	Text
Resource Type	Article

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