Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Diferenciālā epigenoma plaša mēroga asociācijas pētījums× | Kopiju skaita variāciju analīze× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2009–2011 | 1998–2006 |
| Autors≠ | Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014) | Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map) |
| Tips≠ | Comparative epigenome-wide analysis | Genomic structural variant detection pipeline |
| Pirmavots≠ | Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541. link ↗ | 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 ↗ |
| Citi nosaukumi | Differential EWAS, comparative EWAS, epigenome-wide differential methylation analysis, EWAS differential design | CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | A Differential Epigenome-Wide Association Study (Differential EWAS) scans hundreds of thousands of CpG methylation sites across the genome to identify those whose methylation levels differ significantly between two or more comparison groups — such as cases vs. controls, exposed vs. unexposed, or distinct developmental stages. It is the standard epigenomic analogue of a differential expression analysis but operates at the level of DNA methylation marks rather than RNA counts. | 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. |
| ScholarGateDatu kopa ↗ |
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