ArticleBiostatistics (Oxford, England)2014
Parametric modeling of whole-genome sequencing data for CNV identification.
Article in Biostatistics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- A copy number variation detection method based on OCSVM algorithm using multi strategies integration.Scientific reports · 2025Article
- On the core segmentation algorithms of copy number variation detection tools.Briefings in bioinformatics · 2024Article
- PREFACE: In silico pipeline for accurate cell-free fetal DNA fraction prediction.Prenatal diagnosis · 2019Article
- Comprehensively benchmarking applications for detecting copy number variation.PLoS computational biology · 2019Article
- WisecondorX: improved copy number detection for routine shallow whole-genome sequencing.Nucleic acids research · 2019Article
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4 authors.
Funding
Abstract
Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.
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