Rosen Jonathan, Lee Lindsay, Abnousi Armen, Chen Jiawen, Wen Jia, Hu Ming, Li Yun
Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Comput Struct Biotechnol J. 2023 Jan 9;21:931-939. doi: 10.1016/j.csbj.2023.01.003. eCollection 2023.
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.
高通量染色质构象捕获技术，如Hi-C和Micro-C，已实现对染色质空间组织的全基因组视图。最近，基于Hi-C衍生富集的技术，包括HiChIP和PLAC-seq，因其高信噪比和低成本而提供了有吸引力的替代方案。虽然已经为Hi-C数据开发了一系列计算工具，但针对HiChIP和PLAC-seq数据量身定制的方法仍在开发中。在这里，我们介绍了HPTAD，一种从HiChIP和PLAC-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，以证明其相对于为Hi-C数据设计的现有TAD调用程序具有卓越的性能。HPTAD可在https://github.com/yunliUNC/HPTAD上免费获取。
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