Bandingkan metode

	Analisis Variasi Jumlah Salinan Bayesian	Panggilan Varian
Bidang	Bioinformatika	Bioinformatika
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2004–2007	2009–2010 (modern high-throughput era)
Pencetus	Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Tipe	Probabilistic genomic analysis pipeline	Computational genomics pipeline
Sumber perintis	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.	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.
Alias	Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV	SNP calling, genotyping from sequencing, mutation detection, variant detection
Terkait	6	6
Ringkasan	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.	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.