Pattern recognition of topologically associating domains using deep learning

Background Recent 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 conserv...

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Vydáno v:	BMC bioinformatics Ročník 22; číslo Suppl 10; s. 634 - 15
Hlavní autoři:	Yang, Jhen Yuan, Chang, Jia-Ming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 08.12.2022 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Accuracy Algorithms Animals Artificial neural networks Bioinformatics Biomedical and Life Sciences Cell lines Chromosome organization Chromosomes Classification Computational Biology/Bioinformatics Computer Appl. in Life Sciences Deep Learning Domains Evolution Gene mapping Genetic research Genomes Genomics Hi-C Humans Image classification Interactomes Life Sciences Machine learning Methods Mice Microarrays Neural networks Nucleotide sequence Object recognition Object recognition (Computers) Pattern recognition Performance evaluation Structure Synteny TAD Topologically associating domain Taiwan Deep learning Topologically associating domain Hi-C TAD Chromosome organization
ISSN:	1471-2105, 1471-2105
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Popis
Shrnutí:	Background Recent 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? Results To 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. Conclusions TADs 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-022-05075-1