Computational and Structural Biotechnology Journal (Jan 2023)

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

Journal volume & issue
Vol. 21
pp. 931 – 939

Abstract

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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.

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