ArticleNature genetics2025
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 papers.
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Who cites it
184 citing papers in PubMed.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Single-Nucleus Multi-Omic Atlas Maps Regulatory Architecture and Non-Coding Variant Effects across Adult Human Tissues.bioRxiv : the preprint server for biology · 2026Article
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- The power of theory in the life sciences.eLife · 2026Article
- GLM-Prior: a genomic language model for transferable sequence-derived priors in gene regulatory network inference.Nature communications · 2026Article
- Small Activating RNAs: A Curated Database and an Overview of Rational Design Principles.Molecules (Basel, Switzerland) · 2026Review
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- GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles.Nucleic acids research · 2026Article
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- Uniform processing and analysis of IGVF massively parallel reporter assay data with MPRAsnakeflow.Genome research · 2026Article
- Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo.Nature genetics · 2026Article
- A distinct effector B cell population drives autoantibody production in SARS-CoV-2 infection.Immunity · 2026Article
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Mechanistic machine learning for prediction of prime editing outcomes.Nature biotechnology · 2026Article
- A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation.Plant physiology · 2026Article
- DeepGeSeq: deep learning library for genomic sequence modeling and analysis.Bioinformatics (Oxford, England) · 2026Article
- Tools and tactics for studying alternative splicing.Nature reviews. Genetics · 2026Review
- GUANinE v1.1 reveals complementarity of supervised and genomic language models.Bioinformatics (Oxford, England) · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- ExoShorkie: predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning.Bioinformatics (Oxford, England) · 2026Article
124 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. Using statistics derived from Borzoi's predicted coverage, we isolate and accurately score DNA variant effects across multiple layers of regulation, including transcription, splicing and polyadenylation. Evaluated on quantitative trait loci, Borzoi is competitive with and often outperforms state-of-the-art models trained on individual regulatory functions. By applying attribution methods to the derived statistics, we extract cis-regulatory motifs driving RNA expression and post-transcriptional regulation in normal tissues. The wide availability of RNA-seq data across species, conditions and assays profiling specific aspects of regulation emphasizes the potential of this approach to decipher the mapping from DNA sequence to regulatory function.
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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.