dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

dpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes. Due to the large-scale sequencing and cost control whole-genome sequencing (WGS) data, low-coverage data is favorably dispo...

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Bibliographic Details
Main Authors: Yaoyao Li, Junying Zhang, Xiguo Yuan, Junping Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Copy number variation
Dirichlet process
Gaussian mixture model
read depth
low coverage
Online Access:https://ieeexplore.ieee.org/document/8984306/

Internet

https://ieeexplore.ieee.org/document/8984306/

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