Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Miraldi, ER; Pokrovskii, M; Watters, A; Castro, DM; De Veaux, N; Hall, JA; Lee, J-Y; Ciofani, M; Madar, A; Carriero, N; Littman, DR; Bonneau, R

Published Date

  • March 2019

Published In

Volume / Issue

  • 29 / 3

Start / End Page

  • 449 - 463

PubMed ID

  • 30696696

Pubmed Central ID

  • 30696696

Electronic International Standard Serial Number (EISSN)

  • 1549-5469

Digital Object Identifier (DOI)

  • 10.1101/gr.238253.118

Language

  • eng

Conference Location

  • United States