Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Bayesiläinen kopioanumeroanalyysi× | Kopiolukuvaihtelun analyysi – CNV:n havaitseminen ja tulkinta× | |
|---|---|---|
| Tieteenala | Bioinformatiikka | Bioinformatiikka |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2004–2007 | 1998–2006 |
| Kehittäjä≠ | Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Tyyppi≠ | Probabilistic genomic analysis pipeline | Genomic structural variant detection pipeline |
| Alkuperäislähde≠ | Colella, S., Yau, C., Taylor, J. M., Mirza, G., Butler, H., Clouston, P., Bassett, A. S., Seller, A., Holmes, C. C., & Ragoussis, J. (2007). QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Research, 35(6), 2013–2025. 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 ↗ |
| Rinnakkaisnimet | Bayesian CNV analysis, Bayesian CNV calling, probabilistic CNV detection, Bayesian HMM-CNV | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approach yields posterior probabilities for each copy-number state along the genome, providing statistically principled uncertainty quantification that frequentist segmentation methods lack. | 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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