SnapHiC2: A computationally efficient loop caller for single cell Hi-C data

Xiaoqi Li; Lindsay Lee; Armen Abnousi; Miao Yu; Weifang Liu; Le Huang; Yun Li; Ming Hu

doi:10.1016/j.csbj.2022.05.046

SnapHiC2: A computationally efficient loop caller for single cell Hi-C data

Comput Struct Biotechnol J. 2022 Jun 1:20:2778-2783. doi: 10.1016/j.csbj.2022.05.046. eCollection 2022.

Authors

Xiaoqi Li¹, Lindsay Lee², Armen Abnousi², Miao Yu³, Weifang Liu⁴, Le Huang⁵, Yun Li^{4

6

7}, Ming Hu²

Affiliations

¹ Carolina Health Informatics Program, University of North Carolina, Chapel Hill, NC, USA.
² Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
³ State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.
⁴ Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
⁵ Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA.
⁶ Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
⁷ Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.

Abstract

Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC pipeline provides the first tool to map chromatin loops from scHi-C data, but it is computationally intensive. Here we introduce SnapHiC2, which adapts a sliding window approximation when imputing missing contacts in each single cell and reduces both memory usage and computational time by 70%. SnapHiC2 can identify 5 Kb resolution chromatin loops with high sensitivity and accuracy and help to suggest target genes for GWAS variants in a cell-type-specific manner. SnapHiC2 is freely available at: https://github.com/HuMingLab/SnapHiC/releases/tag/v0.2.2.

Keywords: Chromatin loops; Chromatin spatial organization; single cell Hi-C; the random walk with restart (RWR) algorithm.

Abstract

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