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

Emily R. Miraldi, Maria Pokrovskii, Aaron Watters, Dayanne M. Castro, Nicholas De Veaux, Jason A. Hall, June Yong Lee, Maria Ciofani, Aviv Madar, Nick Carriero, Dan R. Littman, Richard Bonneau

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)449-463
Number of pages15
JournalGenome Research
Volume29
Issue number3
DOIs
StatePublished - Mar 1 2019

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Th17 Cells
Gene Regulatory Networks
Transposases
Chromatin
Transcription Factors
Genome
Gene Expression
Benchmarking
Genes
Statistical Factor Analysis
Animal Models
RNA

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. / Miraldi, Emily R.; Pokrovskii, Maria; Watters, Aaron; Castro, Dayanne M.; De Veaux, Nicholas; Hall, Jason A.; Lee, June Yong; Ciofani, Maria; Madar, Aviv; Carriero, Nick; Littman, Dan R.; Bonneau, Richard.

In: Genome Research, Vol. 29, No. 3, 01.03.2019, p. 449-463.

Research output: Contribution to journalArticle

Miraldi, ER, Pokrovskii, M, Watters, A, Castro, DM, De Veaux, N, Hall, JA, Lee, JY, Ciofani, M, Madar, A, Carriero, N, Littman, DR & Bonneau, R 2019, 'Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells', Genome Research, vol. 29, no. 3, pp. 449-463. https://doi.org/10.1101/gr.238253.118
Miraldi ER, Pokrovskii M, Watters A, Castro DM, De Veaux N, Hall JA et al. Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. Genome Research. 2019 Mar 1;29(3):449-463. https://doi.org/10.1101/gr.238253.118
Miraldi, Emily R. ; Pokrovskii, Maria ; Watters, Aaron ; Castro, Dayanne M. ; De Veaux, Nicholas ; Hall, Jason A. ; Lee, June Yong ; Ciofani, Maria ; Madar, Aviv ; Carriero, Nick ; Littman, Dan R. ; Bonneau, Richard. / Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. In: Genome Research. 2019 ; Vol. 29, No. 3. pp. 449-463.
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