小柯机器人

美国德克萨斯大学西南医学中心Jian Zhou近期取得重要工作进展，他研究提出了基于序列的从数千碱基到染色体尺度的三维基因组结构建模。该项研究成果2022年5月12日在线发表于《自然—遗传学》杂志上。

为了了解基因组序列如何影响多尺度三维 (3D) 基因组结构，本文提出了一种基于序列的深度学习方法Orca，它直接根据序列预测从数千碱基到全染色体尺度的3D基因组结构。Orca捕获特定结构依赖性的序列，包括染色质区室和拓扑关联结构域，以及从CTCF介导的不同类型的相互作用到增强子-促进子相互作用和具有细胞类型特异性的Polycomb介导的相互作用。Orca能够实现各种应用，包括预测结构变异对多尺度基因组组织的影响，它重现了不同大小（300bp到90Mb）的实验研究变异的影响。

此外，Orca使计算机虚拟筛选能够在不同尺度上探测3D基因组组织的序列基础。在亚基因组规模上，它预测了细胞类型特异性基因组相互作用的特定转录因子基序。在区块尺度上，序列活动的虚拟筛选提出了一个染色质区块的序列基础模型，其中转录起始位点的作用非常突出。

附：英文原文

Title: Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale

Author: Zhou, Jian

Issue&Volume: 2022-05-12

Abstract: To learn how genomic sequence influences multiscale three-dimensional (3D) genome architecture, this manuscript presents a sequence-based deep-learning approach, Orca, that predicts directly from sequence the 3D genome architecture from kilobase to whole-chromosome scale. Orca captures the sequence dependencies of structures including chromatin compartments and topologically associating domains, as well as diverse types of interactions from CTCF-mediated to enhancer–promoter interactions and Polycomb-mediated interactions with cell-type specificity. Orca enables various applications including predicting structural variant effects on multiscale genome organization and it recapitulated effects of experimentally studied variants at varying sizes (300bp to 90Mb). Moreover, Orca enables in silico virtual screens to probe the sequence basis of 3D genome organization at different scales. At the submegabase scale, it predicted specific transcription factor motifs underlying cell-type-specific genome interactions. At the compartment scale, virtual screens of sequence activities suggest a model for the sequence basis of chromatin compartments with a prominent role of transcription start sites.

DOI: 10.1038/s41588-022-01065-4

Source: https://www.nature.com/articles/s41588-022-01065-4

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
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