Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning
© The Author(s) 2022. Published by Oxford University Press on behalf of American Society of Plant Biologists..
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.
Errataetall: |
CommentIn: Plant Cell. 2022 May 24;34(6):2108-2109. - PMID 35325244 |
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Medienart: |
E-Artikel |
Erscheinungsjahr: |
2022 |
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Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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Enthalten in: |
The Plant cell - 34(2022), 6 vom: 24. Mai, Seite 2174-2187 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Akagi, Takashi [VerfasserIn] |
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Themen: |
Journal Article |
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Anmerkungen: |
Date Completed 27.05.2022 Date Revised 28.03.2023 published: Print CommentIn: Plant Cell. 2022 May 24;34(6):2108-2109. - PMID 35325244 Citation Status MEDLINE |
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doi: |
10.1093/plcell/koac079 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM337895708 |
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520 | |a In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression | ||
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700 | 1 | |a Kuwada, Eriko |e verfasserin |4 aut | |
700 | 1 | |a Takeshita, Kouki |e verfasserin |4 aut | |
700 | 1 | |a Kawakatsu, Taiji |e verfasserin |4 aut | |
700 | 1 | |a Ariizumi, Tohru |e verfasserin |4 aut | |
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