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

Jonathan Rosen; Lindsay Lee; Armen Abnousi; Jiawen Chen; Jia Wen; Ming Hu; Yun Li

doi:10.1016/j.csbj.2023.01.003

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

Comput Struct Biotechnol J. 2023 Jan 9:21:931-939. doi: 10.1016/j.csbj.2023.01.003. eCollection 2023.

Authors

Jonathan Rosen¹, Lindsay Lee², Armen Abnousi², Jiawen Chen³, Jia Wen¹, Ming Hu², Yun Li^{1

3

4}

Affiliations

¹ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
² Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
³ Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
⁴ Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.

Abstract

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.

Keywords: HiChIP; PLAC-seq; Topologically associating domains (TADs).

Abstract

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