ArticleNature communications2024
ECOLE: Learning to call copy number variants on whole exome sequencing data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 21 citations in OpenAlex.
- Likelihood-based optimization enables accurate copy number estimation for paralogous genes using exome data.Bioinformatics (Oxford, England) · 2026Article
- 'Missing' disease-causing variants in Alport syndrome.Nature reviews. Nephrology · 2026Review
- CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data.bioRxiv : the preprint server for biology · 2026Article
- A convolutional attention model classifies copy number variants from whole exome sequencing.Scientific reports · 2026Article
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- ML-ExonCNV: a robust XGBoost multi-expert ensemble framework for rare exon CNV detection in whole-exome sequencing data.Briefings in bioinformatics · 2026Article
- Toward Graph-Based Decoding of Tumor Evolution: Spatial Inference of Copy Number Variations.Diagnostics (Basel, Switzerland) · 2025Article
- araCNA: somatic copy number profiling using long-range sequence models.NAR genomics and bioinformatics · 2025Article
- Mutational Analysis of Early, Low-Grade Bowel Polyps Defines a Subgroup with Concurrent, High-Risk Oncogenic Drivers Independent of Polyp Size.Cancer research communications · 2025Article
- LYCEUM: learning to call copy number variants on low-coverage ancient genomes.Bioinformatics (Oxford, England) · 2025Article
- Benchmarking of germline copy number variant callers from whole genome sequencing data for clinical applications.Bioinformatics advances · 2025Article
- Genetic variations and recurrence in stage III Korean colorectal cancer: Insights from tumor-only mutation analysis.PloS one · 2025Article
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients.NAR genomics and bioinformatics · 2024Article
- Enhancing interpretation of clinical disease-associated copy number variations from multiple sequencing strategies with CNVSeeker.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .
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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.