Linganisha mbinu

	Uchanganuzi wa Mabadiliko ya Idadi ya Nakala kwa Wakati	Uchambuzi wa Tofauti za Idadi ya Nakala
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2010s–present	1998–2006
Mwanzilishi	Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
Aina	Computational genomics pipeline	Genomic structural variant detection pipeline
Chanzo asilia	Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.	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	longitudinal CNV analysis, temporal copy number analysis, time-series CNV profiling, serial CNV analysis	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
Zinazohusiana	5	6
Muhtasari	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.	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.