ArticleGenome research2019
A machine-learning approach for accurate detection of copy number variants from exome sequencing.
Article in Genome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data.bioRxiv : the preprint server for biology · 2026Article
- Clinical and molecular characterization of chondrodysplasias in a cohort of Egyptian patients.Scientific reports · 2025Article
- Detection and characterization of copy-number variants from exome sequencing in the DDD study.Genetics in medicine open · 2024Article
- Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques.Cancers · 2023Article
- Increased Prevalence of Rare Copy Number Variants in Treatment-Resistant Psychosis.Schizophrenia bulletin · 2023Article
- Artificial intelligence-based approaches for the detection and prioritization of genomic mutations in congenital surgical diseases.Frontiers in pediatrics · 2023Review
- Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders-an important consideration for resource-constrained settings.Frontiers in genetics · 2023Review
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning.Nucleic acids research · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Polishing copy number variant calls on exome sequencing data via deep learning.Genome research · 2022Article
- Towards accurate and reliable resolution of structural variants for clinical diagnosis.Genome biology · 2022Review
- Progress in Methods for Copy Number Variation Profiling.International journal of molecular sciences · 2022Review
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Review
- Identification of Copy Number Alterations from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022Article
- Enhancing interpretation of clinical disease-associated copy number variations from multiple sequencing strategies with CNVSeeker.Bioinformatics (Oxford, England) · 2022Article
- Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion.Genome medicine · 2021Article
- Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence.Signal transduction and targeted therapy · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive rates and low concordance because of inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn diagram approaches to identify "high-confidence" CNVs. However, this approach is inadequate, because it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted by four exome-based CNV callers (CANOES, CODEX, XHMM, and CLAMMS) from 503 samples, we demonstrate that CN-Learn identifies true CNVs at higher precision (∼90%) and recall (∼85%) rates while maintaining robust performance even when trained with minimal data (∼30 samples). CN-Learn recovers twice as many CNVs compared to individual callers or Venn diagram-based approaches, with features such as exome capture probe count, caller concordance, and GC content providing the most discriminatory power. In fact, ∼58% of all true CNVs recovered by CN-Learn were either singletons or calls that lacked support from at least one caller. Our study underscores the limitations of current approaches for CNV identification and provides an effective method that yields high-quality CNVs for application in clinical diagnostics.
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