Сравнение на методи

	Байесов анализ на вариациите в броя на копията	Анализ на вариациите в броя на копията
Област	Биоинформатика	Биоинформатика
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2004–2007	1998–2006
Създател	Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Тип	Probabilistic genomic analysis pipeline	Genomic structural variant detection pipeline
Основополагащ източник	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.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Други названия	Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Свързани	6	6
Резюме	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.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.