RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy.
RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy.
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DOI：
10.1007/978-3-030-45257-5_9
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发表时间：
2020-05
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影响因子：
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通讯作者：
Hartemink AJ
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作者：
Mitra S;Zhong J;MacAlpine DM;Hartemink AJ
染色质是细胞核内 DNA 和蛋白质紧密包装的结构。不同蛋白质复合物沿 DNA 的排列会调节基因表达，并受到基因表达的调节。因此，测量不同转录因子（TF）和核小体的结合位置和占据水平对于理解基因调控至关重要。基于抗体的染色质占据率测定方法能够识别特定 DNA 结合因子的结合位点，但一次只能识别一个因子。另一方面，诸如 ATAC-seq、DNase-seq 和 MNase-seq 等表观基因组可及性数据可以深入了解基因组上所有因子的染色质景观，但对这些因子的身份了解很少。在这里，我们提出了 RoboCOP，一种多变量状态空间模型，它将表观基因组可及性数据中的染色质信息与核苷酸序列相结合，以同时计算数百个不同因素的核小体和 TF 占用的全基因组概率得分。 RoboCOP 可应用于任何表观基因组数据集，提供对任何生物体中染色质可及性的定量洞察，但在这里，我们将其应用于 MNase-seq 数据，以阐明酵母基因组中核小体和 150 个 TF 的蛋白质结合情况。使用文献中可用的蛋白质结合数据集，我们表明我们的模型可以更准确地预测这些因素在全基因组范围内的结合。
Chromatin is the tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and level of occupancy of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. On the other hand, epigenomic accessibility data like ATAC-seq, DNase-seq, and MNase-seq provide insight into the chromatin landscape of all factors bound along the genome, but with minimal insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin information from epigenomic accessibility data with nucleotide sequence to compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors at once. RoboCOP can be applied to any epigenomic dataset that provides quantitative insight into chromatin accessibility in any organism, but here we apply it to MNase-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome. Using available protein-binding datasets from the literature, we show that our model more accurately predicts the binding of these factors genome-wide.