CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome
CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome
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CERENKOV：监管非编码变量的计算阐明
DOI：
10.1145/3107411.3107414
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Ramsey, Stephen A.
中科院分区：
文献类型：
--
作者：
Yao, Yao;Liu, Zheng;Singh, Satpreet;Wei, Qi;Ramsey, Stephen A.
我们描述了一种新的计算方法，Cerenkov(调节性非KOd-ing Variome的计算阐明)，用于区分非编码遗传座位中的调节性单核苷酸多态(RSNPs)和非调节性SNPs。Cerenkov是专门为在全基因组关联研究(GWAS)的后分析背景下识别rSNP而设计的；它包括一种新的准确性评分指标(我们称之为平均排名，或AV-GRANK)和一种新的交叉验证策略(基于位点的采样)，这两种策略都正确地解释了GWAS分析后rSNP识别问题的“稀疏阳性袋”性质。我们使用一个由15,331个SNP组成的参考集(OSU17 SNP集)来训练和验证Cerenkov，该参考集的组成基于我们设计的选择标准(连锁不平衡和次要等位基因频率)，以确保与GWAS后分析的相关性。Cerenkov基于一种机器学习算法(梯度增强决策树)，包含了我们从基因组、表观基因组、系统发育和染色质数据集中提取的246个SNP注释特征。Cerenkov包括基于复制时间和DNA形状的新特征。我们发现，为AUPVR性能调整分类器并不能保证AVGRANK的最优性能。我们将切伦科夫与其他九种rSNP识别方法(包括GWAVA、RSVP、DELTA支持向量机、DEEPSEA、EIGEN和DANQ)的验证性能进行了比较，发现在我们测试的所有分类器中，切伦科夫的验证性能是最强的，无论是传统的基于全局排名的指标(⟨AUPVR⟩=0.506；⟨AUROC⟩=0.855)还是AVGRANK(⟨AVGRANK⟩=3.877)。切伦科夫的源代码可在GitHub上找到，SNP功能数据文件可通过Cerenkov网站下载。
We describe a novel computational approach, CERENKOV (Computational Elucidation of the REgulatory NonKOd- ing 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 AV- GRANK) 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 disequi- librium 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 guaran- tee 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.
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