Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data
Subclonal deconvolution in cancer sequencing data is a complex task, and the optimal tools to use are unclear. Here, the authors systematically benchmark subclonal deconvolution pipelines with a comprehensive set of simulated tumour genomes and identify the best-performing methods.
Guardado en:
Autores principales: | Georgette Tanner, David R. Westhead, Alastair Droop, Lucy F. Stead |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f5475b13ac43401197d2270caf88aa9a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Benchmarking of cell type deconvolution pipelines for transcriptomics data
por: Francisco Avila Cobos, et al.
Publicado: (2020) -
Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data
por: Salem Malikic, et al.
Publicado: (2019) -
Author Correction: Benchmarking of cell type deconvolution pipelines for transcriptomics data
por: Francisco Avila Cobos, et al.
Publicado: (2020) -
Quantifying the influence of mutation detection on tumour subclonal reconstruction
por: Lydia Y. Liu, et al.
Publicado: (2020) -
FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples
por: Yao Xiao, et al.
Publicado: (2020)