ArticleThe Plant cell2022
Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.
Article in The Plant cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- PlantSetDelta: Mining regulatory features to enable interpretable gene set analysis in plants.Plant communications · 2026Article
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Investigating cis-regulatory elements and gene expression in multiple tomato varieties using interpretable deep learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Whispers in the genome: The hidden grammar of tomato fruit development.The Plant cell · 2025Article
- Understanding plant phenotypes in crop breeding through explainable AI.Plant biotechnology journal · 2025Review
- DeepWheat: predicting the effects of genomic variants on gene expression and regulatory activities across tissues and varieties in wheat using deep learning.Genome biology · 2025Article
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Arabidopsis as a model for translational research.The Plant cell · 2025Review
- Integrative omics analysis reveals the genetic basis of fatty acid composition in Brassica napus seeds.Genome biology · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Identification of lineage-specific cis-trans regulatory networks related to kiwifruit ripening initiation.The Plant journal : for cell and molecular biology · 2024Article
- Review
- Article
- Deep learning with a small dataset predicts chromatin remodelling contribution to winter dormancy of apple axillary buds.Tree physiology · 2024Article
- Review
- Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.BMC genomics · 2024Article
- Deep learning the cis-regulatory code for gene expression in selected model plants.Nature communications · 2024Article
- Sea-ATI unravels novel vocabularies of plant active cistrome.Nucleic acids research · 2023Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.