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| Maschinelles Lernen-gestützte Kopienzahlvariationsanalyse× | Varianten-Calling× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2010s–present | 2009–2010 (modern high-throughput era) |
| Urheber≠ | Multiple groups; notable early ML-CNV tools include CNV-RF (Bellos et al., 2014) and CANOES (Backenroth et al., 2014) | Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010) |
| Typ≠ | Supervised/unsupervised machine learning pipeline for genomic structural variant detection | Computational genomics pipeline |
| Wegweisende Quelle≠ | Aganezov, S., Goodwin, S., Sherman, R. M., Sedlazeck, F. J., Mehta, G., Rushbrook, S., ... & Schatz, M. C. (2020). Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Research, 30(9), 1258-1273. link ↗ | 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. DOI ↗ |
| Aliasnamen | ML-CNV analysis, ML-based CNV calling, machine learning CNV detection, deep learning CNV analysis | SNP calling, genotyping from sequencing, mutation detection, variant detection |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Machine learning-assisted CNV analysis applies supervised, unsupervised, or deep learning algorithms to detect genomic regions that are duplicated or deleted relative to a reference genome. Rather than relying on fixed statistical thresholds, ML models learn discriminative patterns from read-depth signals, allele frequencies, and other features, substantially improving sensitivity and specificity over classical tools — especially in noisy or low-coverage sequencing data. | 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. |
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