Cargando…
Improving variant calling using population data and deep learning
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In th...
Autores principales: | Chen, Nae-Chyun, Kolesnikov, Alexey, Goel, Sidharth, Yun, Taedong, Chang, Pi-Chuan, Carroll, Andrew |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182612/ https://www.ncbi.nlm.nih.gov/pubmed/37173615 http://dx.doi.org/10.1186/s12859-023-05294-0 |
Ejemplares similares
-
Accurate, scalable cohort variant calls using DeepVariant and GLnexus
por: Yun, Taedong, et al.
Publicado: (2021) -
Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads
por: Shafin, Kishwar, et al.
Publicado: (2021) -
A population-specific reference panel for improved genotype imputation in African Americans
por: O’Connell, Jared, et al.
Publicado: (2021) -
Local read haplotagging enables accurate long-read small variant calling
por: Kolesnikov, Alexey, et al.
Publicado: (2023) -
A deep-learning-based RNA-seq germline variant caller
por: Cook, Daniel E, et al.
Publicado: (2023)