Integrating diverse datasets improves developmental enhancer prediction.
Integrating diverse datasets improves developmental enhancer prediction.
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DOI：
10.1371/journal.pcbi.1003677
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发表时间：
2014-06
影响因子：
4.3
通讯作者：
Capra JA
中科院分区：
文献类型：
--
作者：
Erwin GD;Oksenberg N;Truty RM;Kostka D;Murphy KK;Ahituv N;Pollard KS;Capra JA
基因调控增强子的鉴定采用了多种方法，包括进化保守、调控蛋白结合、染色质修饰和DNA序列基序。为了整合这些不同的方法，我们开发了EnhancerFinder，这是一种从基因组背景中区分发育增强子并预测其组织特异性的两步方法。EnhancerFinder使用多核学习方法来集成DNA序列基序、进化模式和来自各种细胞类型的不同功能基因组数据集。与根据组蛋白标记或单个细胞系中的p300位点定义增强剂的预测方法不同，我们从Vista Enhancer浏览器对EnhancerFinder进行了数百个经过实验验证的人类发育增强剂的培训。我们使用交叉验证对EnhancerFinder进行了全面评估，发现我们的综合方法比考虑单一类型数据的方法更好地识别增强子，例如序列基序、进化保守或增强子相关蛋白的结合。我们发现，在胚胎心脏中活跃的Vista增强剂比在其他几个胚胎组织中活跃的增强剂更容易识别，可能是因为它们独特的高GC含量。我们将EnhancerFinder应用于整个人类基因组，并预测了84,301个发育增强剂及其组织特异性。这些预测为大量的人类非编码DNA提供了特定的功能注释，并显著丰富了在其预测组织中具有注释作用的基因，并导致了全基因组关联研究中的SNP。我们通过对来自三个发育转录因子基因座的新的胚胎基因调控增强子的体内验证，证明了EnhancerFinder预测的实用性。我们的全基因组发育增强子预测作为UCSC基因组浏览器轨道免费提供，我们希望这将使研究人员能够进一步研究发育生物学中的问题。人类基因组中含有大量功能未知的非蛋白质编码DNA。其中一些DNA调控着基因在发育过程中何时、何地以及何种水平上的活跃。增强子是一种调节元件，是DNA的一小段，可以充当开关，在特定的细胞或组织中在特定的时间开启或关闭基因。了解基因组增强子的位置可以提供对发育和疾病的遗传基础的洞察。增强子很难识别，但关于它们位置的线索可以在不同类型的数据中找到，包括DNA序列、进化历史和蛋白质与DNA结合的位置。在这里，我们介绍一种名为EnhancerFinder的新工具，它结合这些数据来预测胚胎发育期间增强子的位置和活性。我们在一大组经过功能验证的人类增强剂上对EnhancerFinder进行了培训，事实证明它非常准确。我们使用EnhancerFinder预测了人类基因组中数以万计的增强子，并验证了老鼠或斑马鱼中三个重要发育基因附近的几个预测。EnhancerFinder的预测将有助于理解隐藏在大量人类非编码DNA中的功能区。
Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. The human genome contains an immense amount of non-protein-coding DNA with unknown function. Some of this DNA regulates when, where, and at what levels genes are active during development. Enhancers, one type of regulatory element, are short stretches of DNA that can act as “switches” to turn a gene on or off at specific times in specific cells or tissues. Understanding where in the genome enhancers are located can provide insight into the genetic basis of development and disease. Enhancers are hard to identify, but clues about their locations are found in different types of data including DNA sequence, evolutionary history, and where proteins bind to DNA. Here, we introduce a new tool, called EnhancerFinder, which combines these data to predict the location and activity of enhancers during embryonic development. We trained EnhancerFinder on a large set of functionally validated human enhancers, and it proved to be very accurate. We used EnhancerFinder to predict tens of thousands of enhancers in the human genome and validated several of the predictions near three important developmental genes in mouse or zebrafish. EnhancerFinder's predictions will be useful in understanding functional regions hidden in the vast amounts of human non-coding DNA.
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影响因子：
30.8
作者：
Aldinger, Kimberly A.;Lehmann, Ordan J.;Hudgins, Louanne;Chizhikov, Victor V.;Bassuk, Alexander G.;Ades, Lesley C.;Krantz, Ian D.;Dobyns, William B.;Millen, Kathleen J.
通讯作者：
Millen, Kathleen J.
影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
3.5
作者：
El-Kasti, Muna M.;Wells, Timothy;Carter, David A.
通讯作者：
Carter, David A.
影响因子：
7
作者：
Gorkin DU;Lee D;Reed X;Fletez-Brant C;Bessling SL;Loftus SK;Beer MA;Pavan WJ;McCallion AS
通讯作者：
McCallion AS
DOI：
10.1007/978-1-60327-241-4_13
发表时间：
2010-01-01
期刊：
DATA MINING TECHNIQUES FOR THE LIFE SCIENCES
影响因子：
--
作者：
Ben-Hur, Asa;Weston, Jason
通讯作者：
Weston, Jason