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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Published in:BMC bioinformatics Vol. 22; no. Suppl 10; pp. 634 - 15
Main Authors: Yang, Jhen Yuan, Chang, Jia-Ming
Format: Journal Article
Language:English
Published: London BioMed Central 08.12.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-05075-1