Pattern recognition of topologically associating domains using deep learning

BMC Bioinformatics

Research

Jhen Yuan Yang¹  Jia-Ming Chang¹ 

[1] Department of Computer Science, National Chengchi University, 11605, Taipei City, Taiwan;

关键词: Topologically associating domain;    TAD;    Hi-C;    Chromosome organization;    Deep learning;   

DOI  :  10.1186/s12859-022-05075-1

 received in 2022-11-07, accepted in 2022-11-22,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundRecent increasing evidence indicates that three-dimensional chromosome structure plays an important role in genomic function. Topologically associating domains (TADs) are self-interacting regions that have been shown to be a chromosomal structural unit. During evolution, these are conserved based on checking synteny block cross species. Are there common TAD patterns across species or cell lines?ResultsTo address the above question, we propose a novel task—TAD recognition—as opposed to traditional TAD identification. Specifically, we treat Hi-C maps as images, thus re-casting TAD recognition as image pattern recognition, for which we use a convolutional neural network and a residual neural network. In addition, we propose an elegant way to generate non-TAD data for binary classification. We demonstrate deep learning performance which is quite promising, AUC > 0.80, through cross-species and cell-type validation.ConclusionsTADs have been shown to be conserved during evolution. Interestingly, our results confirm that the TAD recognition model is practical across species, which indicates that TADs between human and mouse show common patterns from an image classification point of view. Our approach could be a new way to identify TAD variations or patterns among Hi-C maps. For example, TADs of two Hi-C maps are conserved if the two classification models are exchangeable.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

40249_2022_1045_Article_IEq10.gif

40249_2022_1045_Article_IEq8.gif

40249_2022_1045_Article_IEq7.gif

40249_2022_1045_Article_IEq1.gif

Fig. 1

Fig. 4