Linganisha mbinu

	Uchanganuzi wa Mabadiliko ya Idadi Nakala za Kiini Kimoja	Upigaji wa Vigezo
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2011–2015	2009–2010 (modern high-throughput era)
Mwanzilishi	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Aina	Computational genomics pipeline	Computational genomics pipeline
Chanzo asilia	Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.	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.
Majina mbadala	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
Zinazohusiana	6	6
Muhtasari	Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.	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.