Process / pipelineBioinformatics / omics

Bayesian Copy Number Variation Analysis

Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approach yields posterior probabilities for each copy-number state along the genome, providing statistically principled uncertainty quantification that frequentist segmentation methods lack.

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Sources

  1. Colella, S., Yau, C., Taylor, J. M., Mirza, G., Butler, H., Clouston, P., Bassett, A. S., Seller, A., Holmes, C. C., & Ragoussis, J. (2007). QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Research, 35(6), 2013–2025. DOI: 10.1093/nar/gkm076
  2. Fridlyand, J., Snijders, A. M., Pinkel, D., Albertson, D. G., & Jain, A. N. (2004). Hidden Markov models approach to the analysis of array CGH data. Journal of Multivariate Analysis, 90(1), 132–153. DOI: 10.1016/j.jmva.2004.02.008

Related methods

ScholarGateBayesian Copy Number Variation Analysis (Bayesian Copy Number Variation Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/bioinformatics/bayesian-copy-number-variation-analysis