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FISHnet:在单细胞序列寡核苷酸荧光原位杂交成像数据中检测染色质结构域

FISHnet: Detecting chromatin domains in single-cell sequential Oligopaints imaging data.

作者信息

Patel Rohan, Pham Kenneth, Chandrashekar Harshini, Phillips-Cremins Jennifer E

机构信息

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.

Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania.

出版信息

bioRxiv. 2024 Jun 18:2024.06.18.599627. doi: 10.1101/2024.06.18.599627.

Abstract

Sequential Oligopaints DNA FISH is an imaging technique that measures higher-order genome folding at single-allele resolution via multiplexed, probe-based tracing. Currently there is a paucity of algorithms to identify 3D genome features in sequential Oligopaints data. Here, we present FISHnet, a graph theory method based on optimization of network modularity to detect chromatin domains and boundaries in pairwise distance matrices. FISHnet uncovers cell type-specific domain-like folding patterns on single alleles, thus enabling future studies aiming to elucidate the role for single-cell folding variation on genome function.

摘要

连续寡核苷酸荧光原位杂交DNA FISH是一种成像技术,它通过多重探针追踪在单等位基因分辨率下测量高阶基因组折叠。目前,在连续寡核苷酸荧光原位杂交数据中识别三维基因组特征的算法很少。在这里,我们提出了FISHnet,这是一种基于网络模块性优化的图论方法,用于在成对距离矩阵中检测染色质结构域和边界。FISHnet揭示了单等位基因上细胞类型特异性的类似结构域的折叠模式,从而为今后旨在阐明单细胞折叠变异对基因组功能作用的研究提供了可能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7bf/11212945/62d0492d9f24/nihpp-2024.06.18.599627v1-f0001.jpg

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