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| Differential Variant Calling× | Analiza varijacija broja kopija× | |
|---|---|---|
| Oblast | Bioinformatika | Bioinformatika |
| Porodica | Process / pipeline | Process / pipeline |
| Godina nastanka≠ | 2009–2013 (field matured with NGS; seminal tools 2009–2013) | 1998–2006 |
| Tvorac≠ | Multiple groups; key tools: VarScan (Koboldt et al.), MuTect (Cibulskis et al.), GATK Haplotype Caller (DePristo et al.) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Tip≠ | Comparative genomic analysis pipeline | Genomic structural variant detection pipeline |
| Temeljni izvor≠ | Koboldt, D.C., Zhang, Q., Larson, D.E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., & Wilson, R.K. (2012). VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3), 568–576. DOI ↗ | Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454. DOI ↗ |
| Drugi nazivi | somatic variant calling, comparative variant analysis, tumor-normal variant calling, differential SNV/indel calling | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Srodne≠ | 2 | 6 |
| Sažetak≠ | Differential variant calling is a bioinformatics pipeline that identifies genetic variants — single nucleotide variants (SNVs), small insertions/deletions (indels), and structural variants — that are present in one biological sample or condition but absent (or significantly enriched) in a paired reference sample. The canonical application is tumor-versus-normal cancer genomics, where somatic mutations unique to the tumor are distinguished from germline variants shared with normal tissue. The same logic applies to comparing treated vs. untreated cell lines, evolved vs. ancestral strains, or case vs. control cohorts in population genomics. | 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. |
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