Identification of genes under dynamic post-transcriptional regulation from time-series epigenomic data.
Epigenomics
; 11(6): 619-638, 2019 05.
Article
en En
| MEDLINE
| ID: mdl-31044623
ABSTRACT
Aim:
Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials &methods:
We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. Results &conclusion:
The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting Akr1c14 and Il1rl1.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento Postranscripcional del ARN
/
Regulación de la Expresión Génica
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Epigenomics
Año:
2019
Tipo del documento:
Article
País de afiliación:
Luxemburgo