Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors.
Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors.
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DOI：
10.1186/gb-2012-13-9-r48
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发表时间：
2012-09-26
期刊：
影响因子：
12.3
通讯作者：
Gerstein M
中科院分区：
文献类型：
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作者：
Yip KY;Cheng C;Bhardwaj N;Brown JB;Leng J;Kundaje A;Rozowsky J;Birney E;Bickel P;Snyder M;Gerstein M
转录因子通过结合不同类型的调控元件发挥作用。DNA元件百科全书（ENCODE）项目最近从多种细胞类型的约500个ChIP-seq实验中产生了100多种转录因子的结合数据。虽然大量的数据创造了宝贵的资源，但它仍然非常复杂，同时也不完整，因为它只涵盖了所有人类转录因子的一小部分。作为联盟努力提供数据的简明抽象以促进各种类型的下游分析的一部分，我们构建了通过机器学习方法捕获三种配对类型区域的基因组特征的统计模型：首先，具有活性或非活性结合的区域;其次，具有极高或极低程度的共结合的区域，称为HOT和LOT区域;最后是基因近端或远端的调控模块。从远端调控模块，我们开发了计算管道来识别潜在的增强子，其中许多都经过了实验验证。我们进一步将预测的增强子与潜在的靶转录物和涉及的转录因子联系起来。对于HOT区域，我们发现了一个显着的分数的转录因子结合没有明确的序列基序，并表明这一观察可能与这些地区的强DNA可及性。总的来说，这三对区域在染色体位置、染色质特征、结合它们的因子和细胞类型特异性方面表现出复杂的差异。我们的机器学习方法使我们能够识别可能通用于所有转录因子的特征，包括那些未包含在数据中的特征。
Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors. As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions. Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.
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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.
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64.8
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通讯作者：
Ren, Bing
影响因子：
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作者：
Blanchette, M;Bataille, AR;Robert, FO
通讯作者：
Robert, FO
影响因子：
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Hesselberth, Jay R.;Chen, Xiaoyu;Zhang, Zhihong;Sabo, Peter J.;Sandstrom, Richard;Reynolds, Alex P.;Thurman, Robert E.;Neph, Shane;Kuehn, Michael S.;Noble, William S.;Fields, Stanley;Stamatoyannopoulos, John A.
通讯作者：
Stamatoyannopoulos, John A.
影响因子：
64.8
作者：
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