Li Xiaoqi, Lee Lindsay, Abnousi Armen, Yu Miao, Liu Weifang, Huang Le, Li Yun, Hu Ming
Carolina Health Informatics Program, 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. 2022 Jun 1;20:2778-2783. doi: 10.1016/j.csbj.2022.05.046. eCollection 2022.
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.
单细胞Hi-C（scHi-C）技术能够在单细胞分辨率下直接从复杂组织中研究染色质的空间组织。然而，从单细胞中识别染色质环具有挑战性，这主要是由于数据极其稀疏。我们最近开发的SnapHiC管道提供了第一个从scHi-C数据中绘制染色质环的工具，但它计算量很大。在这里，我们介绍SnapHiC2，它在估算每个单细胞中缺失的接触时采用滑动窗口近似法，将内存使用量和计算时间都减少了70%。SnapHiC2能够以高灵敏度和准确性识别5千碱基分辨率的染色质环，并有助于以细胞类型特异性方式为全基因组关联研究（GWAS）变体推荐靶基因。SnapHiC2可在以下网址免费获取：https://github.com/HuMingLab/SnapHiC/releases/tag/v0.2.2 。
