GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
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GERV：一种用于转录因子结合调控变异生成评估的统计方法。
DOI：
10.1093/bioinformatics/btv565
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Gifford,DavidK
中科院分区：
文献类型：
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作者：
Zeng,Haoyang;Hashimoto,Tatsunori;Kang,DanielD;Gifford,DavidK
动机：在全基因组关联研究中发现的大多数疾病相关变异位于基因组的非编码区，具有调节作用。因此，能够解释的功能性后果的变体是必不可少的，用于确定因果变异的分析全基因组关联studies.Results：我们提出了GERV（生成评估的监管变体），一种新的计算方法，用于预测的监管变体，影响转录因子的结合。GERV从ChIP-seq和DNase-seq数据中学习基于k-mer的转录因子结合生成模型，并通过计算参考和替代等位基因之间预测的ChIP-seq读数的变化来对变体进行评分。通过GERV学习的k-mer比单独的基于基序的方法捕获更多的转录因子结合的序列决定簇，包括转录因子的典型基序和相关的辅因子基序。我们表明，GERV优于现有的方法在预测与等位基因特异性结合相关的单核苷酸多态性。GERV正确预测了连锁单核苷酸多态性中一个经验证的因果变异，并优先考虑先前报道的调节乳腺癌细胞系中FOXA 1结合的变异。因此，GERV提供了一个强大的方法，功能注释和优先顺序的因果变异的实验follow-up analysis.Availability和实施：GERV和相关数据的实施可在http://gerv.csail.mit.edu/.Contact:gifford@mit.eduSupplementary信息：补充数据可在Bioinformaticsonline。
Motivation:The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies.Results:We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor’s canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.Availability and implementation:The implementation of GERV and related data are available at http://gerv.csail.mit.edu/.Contact:gifford@mit.eduSupplementary information:Supplementary data are available atBioinformaticsonline.