Enhanced maps of transcription factor binding sites improve regulatory networks learned from accessible chromatin data

编号 040020703

推送时间 20191007

研究领域 森林培育 

年份 2019 

类型 期刊 

语种 英语

标题 Enhanced maps of transcription factor binding sites improve regulatory networks learned from accessible chromatin data

来源期刊 Plant Physiology

期 第207期

发表时间 20190725

关键词 gene regulatory network;  open chromatin datasets;  transcription factor;  site mapping tools; 

摘要 Determining where transcription factors (TFs) bind in genomes provides insight into which transcriptional programs are active across organs, tissue types, and environmental conditions. Recent advances in high-throughput profiling of regulatory DNA have yielded large amounts of information about chromatin accessibility. Interpreting the functional significance of these datasets requires knowledge of which regulators are likely to bind these regions. This can be achieved by using information about TF binding preferences, or motifs, to identify TF binding events that are likely to be functional. Although different approaches exist to map motifs to DNA sequences, a systematic evaluation of these tools in plants is missing. Here we compare four motif mapping tools widely used in the Arabidopsis (Arabidopsis thaliana) research community and evaluate their performance using chromatin immunoprecipitation datasets for 40 TFs. Downstream gene regulatory network (GRN) reconstruction was found to be sensitive to the motif mapper used. We further show that the low recall of FIMO, one of the most frequently used motif mapping tools, can be overcome by using an Ensemble approach, which combines results from different mapping tools. Several examples are provided demonstrating how the Ensemble approach extends our view on transcriptional control for TFs active in different biological processes. Finally, a protocol is presented to effectively derive more complete cell type-specific GRNs through the integrative analysis of open chromatin regions, known binding site information, and expression datasets. This approach will pave the way to increase our understanding of GRNs in different cellular conditions.

服务人员 孙小满

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