A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the efforts of the Model Organism ENCyclopedia Of DNA Elements (modENCODE) and the model organism Encyclopedia of Regulatory Networks (modERN) consortia to systematically assay TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). These datasets comprise 605 TFs identifying 3.6M sites in the fly and 356 TFs identifying 0.9 M sites in the worm and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed "metapeaks", that larger metapeaks have characteristics of high occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single cell RNA-seq data in a machine learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent-daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing GFP-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell type-specific TF-target relationships.

中文翻译：

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961 果蝇和秀丽隐杆线虫转录因子的结合谱揭示了组织特异性调节关系


基因组中的转录因子 （TF） 结合位点目录对于破译调控关系至关重要。在这里，我们介绍了模式生物 EN DNA 元件百科全书 （modENCODE） 和模式生物调控网络百科全书 （modERN） 联盟的努力成果，以系统地测定两种主要模式生物 Drosophila melanogaster（果蝇）和秀丽隐杆线虫（蠕虫）体内的 TF 结合事件。这些数据集包括 605 个 TF 识别苍蝇中的 3.6M 位点和 356 个 TF 识别蠕虫中的 0.9 M 位点，代表了每个基因组中的大部分调控空间。我们证明 TF 与称为“metapeaks”的簇中的染色质相关，较大的 metapeaks 具有高占用靶 （HOT） 区域的特征，并且由 TF 结合的共有序列基序的重要性取决于 metapeak 的大小和复杂性。在机器学习模型中将 ChIP-seq 数据与单细胞 RNA-seq 数据相结合，确定了在促进特定细胞类型中靶基因表达方面具有突出作用的 TF，甚至在胚胎发生过程中区分亲子细胞。这些数据是社区的丰富资源，应该可以推动和指导未来对 TF 功能的调查。为了促进数据的可访问性和实用性，每种生物体的库存中心都提供了所有表达 GFP 标记的 TF 的菌株。染色质免疫沉淀测序数据可通过 ENCODE 数据协调中心 GEO 和直接界面获得，该界面可快速访问处理后的数据集和摘要分析，以及用于探测细胞类型特异性 TF-靶标关系的小部件。