Linganisha mbinu

	Uiteuzi wa Kiashiria wa Bayesian	Uchambuzi wa Tofauti za Idadi ya Nakala
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009	1998–2006
Mwanzilishi	Mark DePristo, Eric Banks, and the Broad Institute GATK team	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Aina	Probabilistic genomic inference pipeline	Genomic structural variant detection pipeline
Chanzo asilia	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.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
Majina mbadala	Bayesian genotyping, probabilistic variant calling, GATK HaplotypeCaller, Bayesian SNP/indel detection	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Zinazohusiana	6	6
Muhtasari	Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based callers, Bayesian approaches explicitly model sequencing error, mapping uncertainty, and prior genotype frequencies to produce calibrated genotype likelihoods that can be used for downstream filtering and association testing.	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.