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...
Main Authors: | , , , , , , , |
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BMC
2023-10-01
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Series: | Genome Biology |
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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. |
first_indexed | 2024-03-09T15:08:30Z |
format | Article |
id | doaj.art-10976f8f41ce4e7a85230a305a7d854a |
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issn | 1474-760X |
language | English |
last_indexed | 2024-03-09T15:08:30Z |
publishDate | 2023-10-01 |
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series | Genome Biology |
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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