A bayesian mixture model for comparative spectral count data in shotgun proteomics.
Mol Cell Proteomics
; 10(8): M110.007203, 2011 Aug.
Article
en En
| MEDLINE
| ID: mdl-21602509
Recent developments in mass-spectrometry-based shotgun proteomics, especially methods using spectral counting, have enabled large-scale identification and differential profiling of complex proteomes. Most such proteomic studies are interested in identifying proteins, the abundance of which is different under various conditions. Several quantitative methods have recently been proposed and implemented for this purpose. Building on some techniques that are now widely accepted in the microarray literature, we developed and implemented a new method using a Bayesian model to calculate posterior probabilities of differential abundance for thousands of proteins in a given experiment simultaneously. Our Bayesian model is shown to deliver uniformly superior performance when compared with several existing methods.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Teorema de Bayes
/
Proteoma
/
Proteínas de Saccharomyces cerevisiae
/
Modelos Biológicos
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Mol Cell Proteomics
Asunto de la revista:
BIOLOGIA MOLECULAR
/
BIOQUIMICA
Año:
2011
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos