Compară metode

	Analiza Variațiilor Numărului de Copii în Serii Temporale	Variant Calling
Domeniu	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline
Anul apariției	2010s–present	2009–2010 (modern high-throughput era)
Autorul original	Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Tip	Computational genomics pipeline	Computational genomics pipeline
Sursa seminală	Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.	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.
Denumiri alternative	longitudinal CNV analysis, temporal copy number analysis, time-series CNV profiling, serial CNV analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
Înrudite	5	6
Rezumat	Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconstructs the clonal dynamics and evolutionary trajectories driving genome instability, enabling researchers to track how sub-populations expand, contract, or acquire new aberrations over time.	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.