ArticleBMC bioinformatics2014
Copy number variation detection using next generation sequencing read counts.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Exploring copy number alterations (CNAs) to gain new insight into SCLC heterogeneity.Molecular biology reports · 2026Review
- A genome-wide association study of arabinoxylan content in flour of triticale (×Frontiers in plant science · 2026Article
- A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes.Plants (Basel, Switzerland) · 2025Review
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- Twin Phenomena of Hypertrophic Cardiomyopathy: A Reported Case Series.Anatolian journal of cardiology · 2024Review
- Copy number variations at theFrontiers in plant science · 2024Article
- Rapid gene content turnover on the germline-restricted chromosome in songbirds.Nature communications · 2023Article
- Molecular Diagnosis of Hypertrophic Cardiomyopathy (HCM): In the Heart of Cardiac Disease.Journal of clinical medicine · 2022Review
- CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data.Biology · 2021Article
- Copy Number Variant Detection with Low-Coverage Whole-Genome Sequencing Represents a Viable Alternative to the Conventional Array-CGH.Diagnostics (Basel, Switzerland) · 2021Article
- Detection of Genetic Rearrangements in the Regulators of Complement Activation RCA Cluster by High-Throughput Sequencing and MLPA.Methods in molecular biology (Clifton, N.J.) · 2021Article
- KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data.Frontiers in cell and developmental biology · 2021Review
- CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2021Article
- Neurodevelopmental trajectory and modifiers of 16p11.2 microdeletion: A follow-up study of four Chinese children carriers.Molecular genetics & genomic medicine · 2020Article
- The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations.Journal of clinical medicine · 2020Review
- Comparative study of whole exome sequencing-based copy number variation detection tools.BMC bioinformatics · 2020Article
- MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2020Article
- Identification of Structural Variation from NGS-Based Non-Invasive Prenatal Testing.International journal of molecular sciences · 2019Article
- Development and interlaboratory evaluation of a NIST Reference Material RM 8366 for EGFR and MET gene copy number measurements.Clinical chemistry and laboratory medicine · 2019Article
- GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation.Genomics, proteomics & bioinformatics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.
resultsA simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.
conclusionsThe new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.
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