HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.

HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.
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DOI:
10.1016/j.csbj.2023.01.003
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发表时间:
2023
影响因子:
6
通讯作者:
Li, Yun
Li, Yun
中科院分区:
生物学2区
文献类型:
--
作者:
Rosen, Jonathan;Lee, Lindsay;Abnousi, Armen;Chen, Jiawen;Wen, Jia;Hu, Ming;Li, Yun

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高通量染色质构象捕获技术,如Hi-C和Micro-C,使染色质空间组织的全基因组视图成为可能。最近,hi - c衍生的基于富集的技术,包括HiChIP和place -seq,由于其高信噪比和低成本,提供了有吸引力的替代方案。虽然已经开发了一系列用于Hi-C数据的计算工具,但针对HiChIP和place -seq数据量身定制的方法仍在开发中。在这里,我们提出了hpad,一种从HiChIP和place -seq数据中识别拓扑相关结构域(tad)的计算方法。我们进行了全面的基准分析,以证明其优于为Hi-C数据设计的现有TAD调用程序。hpad可以在https://github.com/yunliUNC/HPTAD上免费获得。
High-throughput chromatin conformation capture technologies, such as Hi-C and Micro-C, have enabled genome-wide view of chromatin spatial organization. Most recently, Hi-C-derived enrichment-based technologies, including HiChIP and PLAC-seq, offer attractive alternatives due to their high signal-to-noise ratio and low cost. While a series of computational tools have been developed for Hi-C data, methods tailored for HiChIP and PLAC-seq data are still under development. Here we present HPTAD, a computational method to identify topologically associating domains (TADs) from HiChIP and PLAC-seq data. We performed comprehensive benchmark analysis to demonstrate its superior performance over existing TAD callers designed for Hi-C data. HPTAD is freely available at https://github.com/yunliUNC/HPTAD.
用于分析HI-C数据的二维分割。
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