ArticlePLoS computational biology2019
Comprehensively benchmarking applications for detecting copy number variation.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Pangenomes aid accurate detection of large insertions and deletions from targeted sequencing: the case of cardiomyopathies.Genome medicine · 2026Article
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.Interdisciplinary sciences, computational life sciences · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Review
- DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction.Bioinformatics (Oxford, England) · 2025Article
- Comparative study of tools for copy number variation detection using next-generation sequencing data.Scientific reports · 2025Article
- Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.BMC medical informatics and decision making · 2025Article
- Enhancing nanopore adaptive sampling for PromethION using readfish at scale.Genome research · 2025Article
- LncRNA Recognition-Associated CpG Island Detection and Methylation Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Review
- Whole-genome sequencing of copy number variation analysis in Ethiopian cattle reveals adaptations to diverse environments.BMC genomics · 2024Article
- CpG Island Definition and Methylation Mapping of the T2T-YAO Genome.Genomics, proteomics & bioinformatics · 2024Article
- A Review of the Application of Spatial Transcriptomics in Neuroscience.Interdisciplinary sciences, computational life sciences · 2024Review
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Read-depth based approach on whole genome resequencing data reveals important insights into the copy number variation (CNV) map of major global buffalo breeds.BMC genomics · 2023Article
- Targeted nanopore sequencing enables complete characterisation of structural deletions initially identified using exon-based short-read sequencing strategies.Molecular genetics & genomic medicine · 2023Article
- Test development, optimization and validation of a WGS pipeline for genetic disorders.BMC medical genomics · 2023Article
- Identification of Extremely Rare Pathogenic CNVs by Array CGH in Saudi Children with Developmental Delay, Congenital Malformations, and Intellectual Disability.Children (Basel, Switzerland) · 2023Article
- Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis.Frontiers in immunology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationRecently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.
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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.