ArticleGenome research2022
Polishing copy number variant calls on exome sequencing data via deep learning.
Article in Genome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data.bioRxiv : the preprint server for biology · 2026Article
- 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
- Should Scotland provide genome-wide sequencing for the diagnosis of rare developmental disorders? A cost-effectiveness analysis.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Article
- CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics.Bioinformatics (Oxford, England) · 2024Article
- ECOLE: Learning to call copy number variants on whole exome sequencing data.Nature communications · 2024Article
- Identification of copy number variants contributing to hallux valgus.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model,
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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.