A probabilistic approach to learn chromatin architecture and accurate inference of the NF-κB/RelA regulatory network using ChIP-Seq

Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for iterative learning of regulatory networks. For every possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative distance and orientation are calculated to learn which TFBSs are most likely t...

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Published in:Nucleic acids research 2013-08, Vol.41 (15), p.7240-7259
Main Authors: Yang, Jun, Mitra, Abhishek, Dojer, Norbert, Fu, Shuhua, Rowicka, Maga, Brasier, Allan R
Format: Article
Language:English
Subjects:
Alu Elements
Binding Sites
Cell Line, Tumor
Chromatin - genetics
Chromatin - metabolism
Chromatin Assembly and Disassembly
Chromatin Immunoprecipitation - methods
Gene Regulation, Chromatin and Epigenetics
Gene Regulatory Networks
Genome, Human
Humans
Models, Statistical
Molecular Sequence Annotation
NF-kappa B - analysis
NF-kappa B - genetics
Nucleotide Motifs
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, RNA
Transcription Factor RelA - genetics
Transcription Factor RelA - metabolism
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Summary:Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for iterative learning of regulatory networks. For every possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative distance and orientation are calculated to learn which TFBSs are most likely to regulate a given gene. Weighted TFBS contributions to putative gene regulation are integrated to derive an NF-κB gene network. A de novo motif enrichment analysis uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-κB/RelA TFBSs. Comparison with experimental ENCODE ChIP-Seq data indicates that experimental TFBSs highly correlate with predicted sites. We observe that RelA-SP1-enriched promoters have distinct expression profiles from that of RelA-AP1 and are enriched in introns, CpG islands and DNase accessible sites. Sixteen novel NF-κB/RelA-regulated genes and TFBSs were experimentally validated, including TANK, a negative feedback gene whose expression is NF-κB/RelA dependent and requires a functional interaction with the AP1 TFBSs. Our probabilistic method yields more accurate NF-κB/RelA-regulated networks than a traditional, distance-based approach, confirmed by both analysis of gene expression and increased informativity of Genome Ontology annotations. Our analysis provides new insights into how co-occurring TFBSs and local chromatin context orchestrate activation of NF-κB/RelA sub-pathways differing in biological function and temporal expression patterns.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkt493