The evolution and mutational robustness of chromatin accessibility in Drosophila

Abstract Background The evolution of genomic regulatory regions plays a critical role in shaping the diversity of life. While this process is primarily sequence-dependent, the enormous complexity of biological systems complicates the understanding of the factors underlying regulation and its evoluti...

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Main Authors: Samuel Khodursky, Eric B. Zheng, Nicolas Svetec, Sylvia M. Durkin, Sigi Benjamin, Alice Gadau, Xia Wu, Li Zhao
Format: Article
Language:English
Published: BMC 2023-10-01
Series:Genome Biology
Subjects:
Online Access:https://doi.org/10.1186/s13059-023-03079-5
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author Samuel Khodursky
Eric B. Zheng
Nicolas Svetec
Sylvia M. Durkin
Sigi Benjamin
Alice Gadau
Xia Wu
Li Zhao
author_facet Samuel Khodursky
Eric B. Zheng
Nicolas Svetec
Sylvia M. Durkin
Sigi Benjamin
Alice Gadau
Xia Wu
Li Zhao
author_sort Samuel Khodursky
collection DOAJ
description Abstract Background The evolution of genomic regulatory regions plays a critical role in shaping the diversity of life. While this process is primarily sequence-dependent, the enormous complexity of biological systems complicates the understanding of the factors underlying regulation and its evolution. Here, we apply deep neural networks as a tool to investigate the sequence determinants underlying chromatin accessibility in different species and tissues of Drosophila. Results We train hybrid convolution-attention neural networks to accurately predict ATAC-seq peaks using only local DNA sequences as input. We show that our models generalize well across substantially evolutionarily diverged species of insects, implying that the sequence determinants of accessibility are highly conserved. Using our model to examine species-specific gains in accessibility, we find evidence suggesting that these regions may be ancestrally poised for evolution. Using in silico mutagenesis, we show that accessibility can be accurately predicted from short subsequences in each example. However, in silico knock-out of these sequences does not qualitatively impair classification, implying that accessibility is mutationally robust. Subsequently, we show that accessibility is predicted to be robust to large-scale random mutation even in the absence of selection. Conversely, simulations under strong selection demonstrate that accessibility can be extremely malleable despite its robustness. Finally, we identify motifs predictive of accessibility, recovering both novel and previously known motifs. Conclusions These results demonstrate the conservation of the sequence determinants of accessibility and the general robustness of chromatin accessibility, as well as the power of deep neural networks to explore fundamental questions in regulatory genomics and evolution.
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spelling doaj.art-10976f8f41ce4e7a85230a305a7d854a2023-11-26T13:29:19ZengBMCGenome Biology1474-760X2023-10-0124112710.1186/s13059-023-03079-5The evolution and mutational robustness of chromatin accessibility in DrosophilaSamuel Khodursky0Eric B. Zheng1Nicolas Svetec2Sylvia M. Durkin3Sigi Benjamin4Alice Gadau5Xia Wu6Li Zhao7Laboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityLaboratory of Evolutionary Genetics and Genomics, The Rockefeller UniversityAbstract Background The evolution of genomic regulatory regions plays a critical role in shaping the diversity of life. While this process is primarily sequence-dependent, the enormous complexity of biological systems complicates the understanding of the factors underlying regulation and its evolution. Here, we apply deep neural networks as a tool to investigate the sequence determinants underlying chromatin accessibility in different species and tissues of Drosophila. Results We train hybrid convolution-attention neural networks to accurately predict ATAC-seq peaks using only local DNA sequences as input. We show that our models generalize well across substantially evolutionarily diverged species of insects, implying that the sequence determinants of accessibility are highly conserved. Using our model to examine species-specific gains in accessibility, we find evidence suggesting that these regions may be ancestrally poised for evolution. Using in silico mutagenesis, we show that accessibility can be accurately predicted from short subsequences in each example. However, in silico knock-out of these sequences does not qualitatively impair classification, implying that accessibility is mutationally robust. Subsequently, we show that accessibility is predicted to be robust to large-scale random mutation even in the absence of selection. Conversely, simulations under strong selection demonstrate that accessibility can be extremely malleable despite its robustness. Finally, we identify motifs predictive of accessibility, recovering both novel and previously known motifs. Conclusions These results demonstrate the conservation of the sequence determinants of accessibility and the general robustness of chromatin accessibility, as well as the power of deep neural networks to explore fundamental questions in regulatory genomics and evolution.https://doi.org/10.1186/s13059-023-03079-5Deep learningDrosophilaATAC-seqChromatin accessibilityIn silico mutagenesisRobustness
spellingShingle Samuel Khodursky
Eric B. Zheng
Nicolas Svetec
Sylvia M. Durkin
Sigi Benjamin
Alice Gadau
Xia Wu
Li Zhao
The evolution and mutational robustness of chromatin accessibility in Drosophila
Genome Biology
Deep learning
Drosophila
ATAC-seq
Chromatin accessibility
In silico mutagenesis
Robustness
title The evolution and mutational robustness of chromatin accessibility in Drosophila
title_full The evolution and mutational robustness of chromatin accessibility in Drosophila
title_fullStr The evolution and mutational robustness of chromatin accessibility in Drosophila
title_full_unstemmed The evolution and mutational robustness of chromatin accessibility in Drosophila
title_short The evolution and mutational robustness of chromatin accessibility in Drosophila
title_sort evolution and mutational robustness of chromatin accessibility in drosophila
topic Deep learning
Drosophila
ATAC-seq
Chromatin accessibility
In silico mutagenesis
Robustness
url https://doi.org/10.1186/s13059-023-03079-5
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