ArticleNucleic acids research2019
Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
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- Metabolic reprogramming and functional crosstalk within the tumor microenvironment (TME) and A Multi-omics anticancer approach.Medical oncology (Northwood, London, England) · 2025Review
- Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer's disease spectrum.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- An integrative approach to prioritize candidate causal genes for complex traits in cattle.PLoS genetics · 2025Article
- Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.Science advances · 2025Article
- Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.PLoS computational biology · 2024Article
- Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Briefings in bioinformatics · 2024Review
- Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations.Genetics, selection, evolution : GSE · 2024Article
- Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease.Biology of sex differences · 2024Article
- SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.Bioinformatics (Oxford, England) · 2024Article
- Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.bioRxiv : the preprint server for biology · 2024Article
- SuPreMo: a computational tool for streamliningbioRxiv : the preprint server for biology · 2023Article
- Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease.medRxiv : the preprint server for health sciences · 2023Article
- Johann Gregor Mendel: the victory of statistics over human imagination.European journal of human genetics : EJHG · 2023Review
- Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning.Nature biomedical engineering · 2023Article
- Computational Assessment of the Expression-modulating Potential for Non-coding Variants.Genomics, proteomics & bioinformatics · 2023Article
- EvoAug: improving generalization and interpretability of genomic deep neural networks with evolution-inspired data augmentations.Genome biology · 2023Article
- DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.Bioinformatics (Oxford, England) · 2022Article
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5 authors.
Funding
Abstract
Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet, the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here, we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequences for 438 million variants observed in previous sequencing projects. These impact scores are assay-specific, are predictive of allele-specific transcription factor binding and are enriched for variants associated with gene expression and disease risk. Nucleotide-level functional consequence scores for non-coding variants can refine the mechanism of known functional variants, identify novel risk variants and prioritize downstream experiments.
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