Coolpup.py: versatile pile-up analysis of Hi-C data
Coolpup.py: versatile pile-up analysis of Hi-C data
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DOI：
10.1093/bioinformatics/btaa073
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发表时间：
2020-05-15
期刊：
影响因子：
5.8
通讯作者：
Bickmore, Wendy A.
中科院分区：
文献类型：
--
作者：
Flyamer, Ilya M.;Illingworth, Robert S.;Bickmore, Wendy A.
动机：Hi-C 是目前研究基因组全局 3D 组织的首选方法。 Hi-C 的一个主要限制是稳健检测数据中的循环所需的测序深度。即使在单细胞 Hi-C 数据中，用于缓解此问题的一种流行方法是对高分辨率数据集中注释的峰或其他特征进行全基因组平均（堆积），以衡量它们在不太深入的测序数据中的突出程度。然而，当前的工具没有提供这种方法的计算高效且多功能的实现。结果：在这里，我们描述了coolpup.py——一种对 Hi-C 数据执行堆积分析的多功能工具。我们通过复制先前发表的有关粘连蛋白和 CTCF 在 3D 基因组组织中的作用的发现，以及发现 Polycomb 驱动的相互作用的新细节来证明其实用性。我们还提出了堆积方法的一种新颖变体，可以帮助循环交互的统计分析。我们预计，coolpup.py 将通过允许易于使用、多功能且高效的堆积生成来帮助 Hi-C 数据分析。
Motivation: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach.Results: Here, we describe coolpup.py-a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions. We also present a novel variation of the pile-up approach that can aid the statistical analysis of looping interactions. We anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pile-ups.