ArticleScientific reports2018
MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.
Article in Scientific reports, 2018. 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, 18 citations in OpenAlex.
- Population genomic insights into the domestication ofHorticulture research · 2026Article
- Population-level gene copy number variations reveal distinct genetic properties of different Malus species.BMC genomics · 2025Article
- Comparative analysis of hybrid-SNP microarray and nanopore sequencing for detection of large-sized copy number variants in the human genome.Molecular cytogenetics · 2025Article
- Inferring the Selective History of CNVs Using a Maximum Likelihood Model.Genome biology and evolution · 2025Article
- Clinical Genetic Screening in Adult Patients with Kidney Disease.Clinical journal of the American Society of Nephrology : CJASN · 2020Review
- Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.PLoS computational biology · 2020Article
- A Survey of Copy Number Variation in the Porcine Genome Detected From Whole-Genome Sequence.Frontiers in genetics · 2019Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
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Abstract
Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.
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