染色质
基因组
计算生物学
电池类型
DNA
功能(生物学)
DNA测序
类型(生物学)
计算机科学
生物
细胞
遗传学
基因
生态学
作者
Alan Min,Jacob Schreiber,Anshul Kundaje,William Stafford Noble
标识
DOI:10.1101/2024.04.12.589240
摘要
Abstract Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets—each represented as a 2D contact map— spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.
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