ArticleNucleic acids research2021
PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 53 citations in OpenAlex.
- Deep Learning for Deciphering the Plant Cis-Regulatory Code.Plants (Basel, Switzerland) · 2026Review
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- How WRKY transcription factors fine-tune specificity in plant stress responses: from W-box to regulatory code.Molecular biology reports · 2026Review
- The landscape of regulatory element evolution in a C4 perennial grass.BMC genomics · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- AI-assisted crop improvement: new design tools within established regulatory frameworks.Frontiers in plant science · 2026Article
- Sequence-based chromatin activity modeling and regulatory impact prediction of genetic variants in farmed animals using deep learning.NAR genomics and bioinformatics · 2025Article
- A Natural Major Module Confers the Trade-Off between Phenotypic Mean and Plasticity of Grain Chalkiness in Rice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Rational design of promoter editing confers multipathogen resistance in rice.Genome biology · 2025Article
- 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
- deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants.Plants (Basel, Switzerland) · 2025Article
- Precise engineering of gene expression by editing plasticity.Genome biology · 2025Article
- Combining AI and new genomic techniques to 'fine-tune' plants: challenges in risk assessment.Frontiers in plant science · 2025Article
- LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.Briefings in bioinformatics · 2024Article
- DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.Plant communications · 2024Article
- Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits.Nature communications · 2024Article
- A foundational large language model for edible plant genomes.Communications biology · 2024Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.
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Registered trials
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 OpenQuestion graph.