Reeder Christopher, Closser Michael, Poh Huay Mei, Sandhu Kuljeet, Wichterle Hynek, Gifford David
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Departments of Pathology and Cell Biology, Neurology, and Neuroscience, Center for Motor Neuron Biology and Disease, Columbia Stem Cell Initiative, Columbia University Medical Center, New York, New York, United States of America.
PLoS One. 2015 May 13;10(5):e0122420. doi: 10.1371/journal.pone.0122420. eCollection 2015.
RNA Polymerase II ChIA-PET data has revealed enhancers that are active in a profiled cell type and the genes that the enhancers regulate through chromatin interactions. The most commonly used computational method for analyzing ChIA-PET data, the ChIA-PET Tool, discovers interaction anchors at a spatial resolution that is insufficient to accurately identify individual enhancers. We introduce Germ, a computational method that estimates the likelihood that any two narrowly defined genomic locations are jointly occupied by RNA Polymerase II. Germ takes a blind deconvolution approach to simultaneously estimate the likelihood of RNA Polymerase II occupation as well as a model of the arrangement of read alignments relative to locations occupied by RNA Polymerase II. Both types of information are utilized to estimate the likelihood that RNA Polymerase II jointly occupies any two genomic locations. We apply Germ to RNA Polymerase II ChIA-PET data from embryonic stem cells to identify the genomic locations that are jointly occupied along with transcription start sites. We show that these genomic locations align more closely with features of active enhancers measured by ChIP-Seq than the locations identified using the ChIA-PET Tool. We also apply Germ to RNA Polymerase II ChIA-PET data from motor neuron progenitors. Based on the Germ results, we observe that a combination of cell type specific and cell type independent regulatory interactions are utilized by cells to regulate gene expression.
RNA聚合酶II染色质相互作用分析结合成对末端标签（ChIA-PET）数据揭示了在特定细胞类型中活跃的增强子以及这些增强子通过染色质相互作用调控的基因。分析ChIA-PET数据最常用的计算方法——ChIA-PET工具，在空间分辨率上发现相互作用锚点，该分辨率不足以准确识别单个增强子。我们引入了Germ，这是一种计算方法，用于估计任意两个精确定义的基因组位置被RNA聚合酶II共同占据的可能性。Germ采用盲反卷积方法，同时估计RNA聚合酶II占据的可能性以及相对于RNA聚合酶II占据位置的读段比对排列模型。这两类信息都用于估计RNA聚合酶II共同占据任意两个基因组位置的可能性。我们将Germ应用于胚胎干细胞的RNA聚合酶II ChIA-PET数据，以识别与转录起始位点共同占据的基因组位置。我们表明，与使用ChIA-PET工具识别的位置相比，这些基因组位置与通过染色质免疫沉淀测序（ChIP-Seq）测量的活跃增强子特征更紧密地对齐。我们还将Germ应用于运动神经元祖细胞的RNA聚合酶II ChIA-PET数据。基于Germ的结果，我们观察到细胞利用细胞类型特异性和细胞类型非依赖性调控相互作用的组合来调控基因表达。
