ArticleNature biomedical engineering2023
Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning.
Article in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- Integrated profiling of metaplastic breast cancer identifies putative master regulators of intratumoral heterogeneity.NPJ breast cancer · 2025Article
- Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.British journal of clinical pharmacology · 2025Review
- Article
- Fundamentals for predicting transcriptional regulations from DNA sequence patterns.Journal of human genetics · 2024Review
- Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots.Nature communications · 2024Article
- Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.Science advances · 2024Article
- Unleashing the potential: AI empowered advanced metasurface research.Nanophotonics (Berlin, Germany) · 2024Review
- Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.Briefings in bioinformatics · 2024Article
- Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure.Cell reports · 2023Article
- Atlas of primary cell-type-specific sequence models of gene expression and variant effects.Cell reports methods · 2023Article
- Toward a comprehensive catalog of regulatory elements.Human genetics · 2023Review
- Predicting pathogenicity from non-coding mutations.Nature biomedical engineering · 2023Article
- A novel CCDC91 isoform associated with ossification of the posterior longitudinal ligament of the spine works as a non-coding RNA to regulate osteogenic genes.American journal of human genetics · 2023Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.
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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 OpenQuestion graph.