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BINDER通过全面表征共有TAD边界实现了对分层TAD的准确识别。

BINDER achieves accurate identification of hierarchical TADs by comprehensively characterizing consensus TAD boundaries.

作者信息

Liu Yangyang, Liu Bingqiang, Liu Juntao

机构信息

School of Mathematics and Statistics, Shandong University (Weihai), Weihai, 264209, China.

School of Mathematics, Shandong University, Jinan, 250100, China

出版信息

Genome Res. 2025 May 2;35(5):1194-1208. doi: 10.1101/gr.279647.124.

Abstract

As crucial chromatin structures, hierarchical TADs play important roles in epigenetic organization, transcriptional activity, gene regulation, and cell differentiation. Currently, it remains a highly challenging task to accurately identify hierarchical TADs in a computational manner. The key bottleneck for existing TAD callers lies in the difficulty in the prediction of precise TAD boundaries. We solve this problem by introducing a novel algorithm, called BINDER, which conducts a boundary consensus approach, and then precisely locate hierarchical TAD boundaries by developing a multifaceted boundary characterization strategy. In comparison with other leading TAD callers, BINDER shows significant improvement in identifying hierarchical TADs and exhibits the strongest robustness with ultrasparse data, which supports the importance of boundary identification in calling hierarchical TADs. Applying BINDER to experimental data and mouse hematopoietic cases, we find that the hierarchical TADs identified by BINDER show strong biological relevance in their epigenetic organization, transcriptional activity, DNA motifs, and coregulation during cellular differentiation. BINDER discovers differences in the enrichment of two specific transcription factors, CHD1 and CHD2, at TAD boundaries with different hierarchies. It also observes variations in the gene expression of TADs with different hierarchies during cellular differentiation.

摘要

作为关键的染色质结构,层级拓扑相关结构域(TADs)在表观遗传组织、转录活性、基因调控和细胞分化中发挥着重要作用。目前,以计算方式准确识别层级TADs仍然是一项极具挑战性的任务。现有TAD调用工具的关键瓶颈在于精确预测TAD边界存在困难。我们通过引入一种名为BINDER的新算法解决了这个问题,该算法采用边界共识方法,然后通过开发多方面的边界特征化策略来精确定位层级TAD边界。与其他领先的TAD调用工具相比,BINDER在识别层级TADs方面有显著改进,并且在超稀疏数据下表现出最强的稳健性,这支持了边界识别在调用层级TADs中的重要性。将BINDER应用于实验数据和小鼠造血案例中,我们发现BINDER识别出的层级TADs在其表观遗传组织、转录活性、DNA基序以及细胞分化过程中的共调控方面显示出很强的生物学相关性。BINDER发现了两种特定转录因子CHD1和CHD2在不同层级的TAD边界处富集的差异。它还观察到在细胞分化过程中不同层级的TADs基因表达的变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6435/12047538/297420f8ad27/1194f01.jpg

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