Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments.
Resolving diverse protein-DNA footprints from exonuclease-based ChIP experiments.
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DOI：
10.1093/bioinformatics/btab274
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发表时间：
2021-07-12
期刊：
影响因子：
--
通讯作者：
Narlikar L
中科院分区：
文献类型：
--
作者：
Biswas A;Narlikar L
基于高通量染色质免疫沉淀（ChIP）测序的检测捕获与谱化转录因子（TF）相关的基因组区域。ChIP-exo是一种改进的方案，它使用lambda外切酶来酶切TF-DNA复合物附近的DNA，以提高TF-DNA接触的位置分辨率。因为消化是在5-3方向上进行的，所以该方案在双链DNA的两侧产生靠近复合体的定向足迹。像所有基于芯片的方法一样，ChIP-exo报告了与TF相关的不同区域的混合物：那些直接绑定到TF的区域以及通过中介连接的区域。然而，足迹的分布很可能是DNA复合体形成的指示。我们提出了ExoDiversity，它使用基于模型的框架来学习足迹和基序的联合分布，从而将ChIP-exo足迹的混合分解为不同的结合模式。它不使用先验基序或TF信息，并自动从数据中学习不同模式的数量。我们展示了它在广泛的tf和生物/细胞类型上的应用。因为它的目标是解释报告区域的完整集合，所以它能够识别在数据集的一小部分中出现的辅因子TF基序。此外，ExoDiversity还发现了标准基序内外的小核苷酸变异，这些变异与足迹的变异共同发生，这表明这些区域的TF-DNA结构配置可能不同。最后，我们发现检测到的模式具有特定的DNA形状特征和保护信号，从而深入了解了假定的TF-DNA复合物的结构和功能。ExoDiversity的代码可在https://github.com/NarlikarLab/exoDIVERSITY上找到。补充数据可在生物信息学网站获得。
High-throughput chromatin immunoprecipitation (ChIP) sequencing-based assays capture genomic regions associated with the profiled transcription factor (TF). ChIP-exo is a modified protocol, which uses lambda exonuclease to digest DNA close to the TF-DNA complex, in order to improve on the positional resolution of the TF-DNA contact. Because the digestion occurs in the 5–3 orientation, the protocol produces directional footprints close to the complex, on both sides of the double stranded DNA. Like all ChIP-based methods, ChIP-exo reports a mixture of different regions associated with the TF: those bound directly to the TF as well as via intermediaries. However, the distribution of footprints are likely to be indicative of the complex forming at the DNA. We present ExoDiversity, which uses a model-based framework to learn a joint distribution over footprints and motifs, thus resolving the mixture of ChIP-exo footprints into diverse binding modes. It uses no prior motif or TF information and automatically learns the number of different modes from the data. We show its application on a wide range of TFs and organisms/cell-types. Because its goal is to explain the complete set of reported regions, it is able to identify co-factor TF motifs that appear in a small fraction of the dataset. Further, ExoDiversity discovers small nucleotide variations within and outside canonical motifs, which co-occur with variations in footprints, suggesting that the TF-DNA structural configuration at those regions is likely to be different. Finally, we show that detected modes have specific DNA shape features and conservation signals, giving insights into the structure and function of the putative TF-DNA complexes. The code for ExoDiversity is available on https://github.com/NarlikarLab/exoDIVERSITY. Supplementary data are available at Bioinformatics online.
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