Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data.
Methods Mol Biol
; 1833: 83-93, 2018.
Article
en En
| MEDLINE
| ID: mdl-30039365
ABSTRACT
CNV detection requires a high-quality segmentation of genomic data. In many WGS experiments, sample and control are sequenced together in a multiplexed fashion using DNA barcoding for economic reasons. Using the differential read depth of these two conditions cancels out systematic additive errors. Due to this detrending, the resulting data is appropriate for inference using a hidden Markov model (HMM), arguably one of the principal models for labeled segmentation. However, while the usual frequentist approaches such as Baum-Welch are problematic for several reasons, they are often preferred to Bayesian HMM inference, which normally requires prohibitively long running times and exceeds a typical user's computational resources on a genome scale data. HaMMLET solves this problem using a dynamic wavelet compression scheme, which makes Bayesian segmentation of WGS data feasible on standard consumer hardware.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Análisis de Secuencia de ADN
/
Código de Barras del ADN Taxonómico
/
Secuenciación de Nucleótidos de Alto Rendimiento
Tipo de estudio:
Health_economic_evaluation
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2018
Tipo del documento:
Article
País de afiliación:
Suecia