Differential peak calling of ChIP-seq signals with replicates with THOR.
Nucleic Acids Res
; 44(20): e153, 2016 Nov 16.
Article
en En
| MEDLINE
| ID: mdl-27484474
ABSTRACT
The study of changes in protein-DNA interactions measured by ChIP-seq on dynamic systems, such as cell differentiation, response to treatments or the comparison of healthy and diseased individuals, is still an open challenge. There are few computational methods comparing changes in ChIP-seq signals with replicates. Moreover, none of these previous approaches addresses ChIP-seq specific experimental artefacts arising from studies with biological replicates. We propose THOR, a Hidden Markov Model based approach, to detect differential peaks between pairs of biological conditions with replicates. THOR provides all pre- and post-processing steps required in ChIP-seq analyses. Moreover, we propose a novel normalization approach based on housekeeping genes to deal with cases where replicates have distinct signal-to-noise ratios. To evaluate differential peak calling methods, we delineate a methodology using both biological and simulated data. This includes an evaluation procedure that associates differential peaks with changes in gene expression as well as histone modifications close to these peaks. We evaluate THOR and seven competing methods on data sets with distinct characteristics from in vitro studies with technical replicates to clinical studies of cancer patients. Our evaluation analysis comprises of 13 comparisons between pairs of biological conditions. We show that THOR performs best in all scenarios.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Cadenas de Markov
/
Análisis de Secuencia de ADN
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Biología Computacional
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Inmunoprecipitación de Cromatina
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Secuenciación de Nucleótidos de Alto Rendimiento
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2016
Tipo del documento:
Article
País de afiliación:
Alemania