Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.
Genome Res
; 29(3): 449-463, 2019 03.
Article
em En
| MEDLINE
| ID: mdl-30696696
ABSTRACT
Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)-seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs ("TF-TF modules") in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fatores de Transcrição
/
Cromatina
/
Redes Reguladoras de Genes
/
Células Th17
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Genome Res
Assunto da revista:
BIOLOGIA MOLECULAR
/
GENETICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos