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Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.
- Source :
-
Genome research [Genome Res] 2019 Mar; Vol. 29 (3), pp. 449-463. Date of Electronic Publication: 2019 Jan 29. - Publication Year :
- 2019
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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.<br /> (© 2019 Miraldi et al.; Published by Cold Spring Harbor Laboratory Press.)
Details
- Language :
- English
- ISSN :
- 1549-5469
- Volume :
- 29
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Genome research
- Publication Type :
- Academic Journal
- Accession number :
- 30696696
- Full Text :
- https://doi.org/10.1101/gr.238253.118