@article{Miraldi01032019,
author = {Miraldi, Emily R. and Pokrovskii, Maria and Watters, Aaron and Castro, Dayanne M. and De Veaux, Nicholas and Hall, Jason A. and Lee, June-Yong and Ciofani, Maria and Madar, Aviv and Carriero, Nick and Littman, Dan R. and Bonneau, Richard}, 
title = {Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells},
volume = {29}, 
number = {3}, 
pages = {449-463}, 
year = {2019}, 
doi = {10.1101/gr.238253.118}, 
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.}, 
URL = {http://genome.cshlp.org/content/29/3/449.abstract}, 
eprint = {http://genome.cshlp.org/content/29/3/449.full.pdf+html}, 
journal = {Genome Research} 
}