Pattern recognition of topologically associating domains using deep learning
Abstract 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 ar...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2022-12-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-022-05075-1 |
_version_ | 1811179038980440064 |
---|---|
author | Jhen Yuan Yang Jia-Ming Chang |
author_facet | Jhen Yuan Yang Jia-Ming Chang |
author_sort | Jhen Yuan Yang |
collection | DOAJ |
description | Abstract 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. |
first_indexed | 2024-04-11T06:27:56Z |
format | Article |
id | doaj.art-f8432215efd744449986f19f08a4dc70 |
institution | Directory Open Access Journal |
issn | 1471-2105 |
language | English |
last_indexed | 2024-04-11T06:27:56Z |
publishDate | 2022-12-01 |
publisher | BMC |
record_format | Article |
series | BMC Bioinformatics |
spelling | doaj.art-f8432215efd744449986f19f08a4dc702022-12-22T04:40:18ZengBMCBMC Bioinformatics1471-21052022-12-0122S1011510.1186/s12859-022-05075-1Pattern recognition of topologically associating domains using deep learningJhen Yuan Yang0Jia-Ming Chang1Department of Computer Science, National Chengchi UniversityDepartment of Computer Science, National Chengchi UniversityAbstract 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.https://doi.org/10.1186/s12859-022-05075-1Topologically associating domainTADHi-CChromosome organizationDeep learning |
spellingShingle | Jhen Yuan Yang Jia-Ming Chang Pattern recognition of topologically associating domains using deep learning BMC Bioinformatics Topologically associating domain TAD Hi-C Chromosome organization Deep learning |
title | Pattern recognition of topologically associating domains using deep learning |
title_full | Pattern recognition of topologically associating domains using deep learning |
title_fullStr | Pattern recognition of topologically associating domains using deep learning |
title_full_unstemmed | Pattern recognition of topologically associating domains using deep learning |
title_short | Pattern recognition of topologically associating domains using deep learning |
title_sort | pattern recognition of topologically associating domains using deep learning |
topic | Topologically associating domain TAD Hi-C Chromosome organization Deep learning |
url | https://doi.org/10.1186/s12859-022-05075-1 |
work_keys_str_mv | AT jhenyuanyang patternrecognitionoftopologicallyassociatingdomainsusingdeeplearning AT jiamingchang patternrecognitionoftopologicallyassociatingdomainsusingdeeplearning |