%0 Journal Article
%T Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells
%A Aaron Watters
%A Aviv Madar
%A Dan R. Littman
%A Dayanne M. Castro
%A Emily R. Miraldi
%A Jason A. Hall
%A June-Yong Lee
%A Maria Ciofani
%A Maria Pokrovskii
%A Nicholas De Veaux
%A Nick Carriero
%A Richard Bonneau
%J Genome Research
%D 2019
%R http://www.genome.org/cgi/doi/10.1101/gr.238253.118
%X 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)¨Cseq, 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 (Ħ°TF¨CTF modulesĦħ) 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
%U https://genome.cshlp.org/content/29/3/449.abstract