ArticleNature methods2023
Cue: a deep-learning framework for structural variant discovery and genotyping.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Detecting pathogenic structural variation in families with undiagnosed rare disease in a national genome project.European journal of human genetics : EJHG · 2026Article
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease.bioRxiv : the preprint server for biology · 2026Article
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- A Murine Database of Structural Variants Identifies A Candidate Gene for a Spontaneous Murine Lymphoma Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Population-level structural variant characterization using pangenome graphs.Nature genetics · 2026Article
- Multi-omics analysis of somatic mutants reveals TCP7 allelically regulates multiple carotenogenic genes in citrus.Molecular horticulture · 2026Article
- Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing.Frontiers in genetics · 2026Review
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- insilicoSV: a flexible grammar-based framework for structural variant simulation and placement.Bioinformatics (Oxford, England) · 2025Article
- Recommendations for bioinformatics in clinical practice.Genome medicine · 2025Review
- Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions.Nature communications · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Blended Length Genome Sequencing (blend-seq): Combining Short Reads with Low-Coverage Long Reads to Maximize Variant Discovery.bioRxiv : the preprint server for biology · 2025Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- Accurate, Scalable Structural Variant Genotyping in Complex Genomes at Population Scales.Molecular biology and evolution · 2025Article
- cuteFC: regenotyping structural variants through an accurate and efficient force-calling method.Genome biology · 2025Article
- CYTO-SV-ML: A Machine Learning Tool for Cytogenetic Structural Variant Analysis in Somatic Cell Type Using Genome Sequences.Life (Basel, Switzerland) · 2025Article
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7 authors.
Funding
Abstract
Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, which cannot scale to the vast diversity of SVs nor fully harness the information available in sequencing datasets. Here we propose an extensible deep-learning framework, Cue, to call and genotype SVs that can learn complex SV abstractions directly from the data. At a high level, Cue converts alignments to images that encode SV-informative signals and uses a stacked hourglass convolutional neural network to predict the type, genotype and genomic locus of the SVs captured in each image. We show that Cue outperforms the state of the art in the detection of several classes of SVs on synthetic and real short-read data and that it can be easily extended to other sequencing platforms, while achieving competitive performance.
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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.