Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model.
Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model.
复制标题
通过集成机器学习模型预测未识别的增强子介导的基因组拓扑
DOI：
10.1101/gr.264606.120
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发表时间：
2020-12
期刊：
影响因子：
7
通讯作者：
Li M
中科院分区：
文献类型：
--
作者：
Tang L;Hill MC;Wang J;Wang J;Martin JF;Li M
转录增强子通常在长基因组距离上工作，以精确调节时空基因表达模式。解剖这些远端调控元件物理接触的启动子对于理解发育过程以及疾病相关风险变异的作用至关重要。现代近端连接分析，如HiChIP和china - pet，有助于准确识别增强子和启动子之间的远程接触。然而，这些检测在技术上具有挑战性，昂贵且耗时，使得难以研究增强子拓扑结构，特别是在未表征的细胞类型中。为了克服这些缺点，我们设计了一个集成机器学习模型LoopPredictor来预测缺乏远程接触图谱的细胞类型的基因组拓扑结构。为了在常见的基因组结构接触上丰富功能增强子-启动子环，我们使用H3K27ac和YY1 HiChIP数据训练LoopPredictor。此外，几个相关的多组学特征的整合有助于识别和注释预测的循环。LoopPredictor能够有效地识别细胞类型特异性增强子介导的环和启动子-启动子相互作用，具有适度的特征输入要求。与实验生成的H3K27ac HiChIP数据相比，我们发现LoopPredictor能够识别功能性增强子环。此外，为了探索LoopPredictor的跨物种预测能力，我们将小鼠多组学特征输入到一个基于人类数据训练的模型中，发现预测的增强子环输出高度保守。LoopPredictor能够分离细胞类型特异性的远程基因调控，并可以加速远端疾病相关风险变异的识别。
Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and time-consuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell types which lack long-range contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. Moreover, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type–specific enhancer-mediated loops, and promoter–promoter interactions, with a modest feature input requirement. Comparable to experimentally generated H3K27ac HiChIP data, we found that LoopPredictor was able to identify functional enhancer loops. Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs were highly conserved. LoopPredictor enables the dissection of cell type–specific long-range gene regulation and can accelerate the identification of distal disease-associated risk variants.
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影响因子：
64.5
作者：
Prescott SL;Srinivasan R;Marchetto MC;Grishina I;Narvaiza I;Selleri L;Gage FH;Swigut T;Wysocka J
通讯作者：
Wysocka J
影响因子：
64.5
作者：
Gasperini, Molly;Hill, Andrew J.;Shendure, Jay
通讯作者：
Shendure, Jay
影响因子：
9.3
作者：
Mariani L;Weinand K;Vedenko A;Barrera LA;Bulyk ML
通讯作者：
Bulyk ML
影响因子：
30.8
作者：
Mumbach MR;Satpathy AT;Boyle EA;Dai C;Gowen BG;Cho SW;Nguyen ML;Rubin AJ;Granja JM;Kazane KR;Wei Y;Nguyen T;Greenside PG;Corces MR;Tycko J;Simeonov DR;Suliman N;Li R;Xu J;Flynn RA;Kundaje A;Khavari PA;Marson A;Corn JE;Quertermous T;Greenleaf WJ;Chang HY
通讯作者：
Chang HY
影响因子：
12.3
作者：
Moore, Jill E.;Pratt, Henry E.;Weng, Zhiping
通讯作者：
Weng, Zhiping