Start from Scratch: Precisely Identify Massive Active Enhancers by Sequencing
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ABSTRACT:
PROVIDER: PRJNA531411 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8864185.fastq.gz | Fastqsanger.gz | |||
| SRR8864185_1.fastq.gz | Fastqsanger.gz | |||
| SRR8864185_2.fastq.gz | Fastqsanger.gz |
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Project description:Enhancer loci identified by ChIP-Seq or other experimental methods occupy hundreds of base pairs on the genome. It is the paradox comparing with the motif analysis, which usually contains only a few or tens nucleotides, achieved by bioinformatics analysis. To address this issue, we develop an experimental method, termed as massive active enhancer sequencing (MAE-Seq), to designate active enhancer sequences from arbitrary sources of 25bp random DNA libraries. These sequences are constructed in a mini-promoter vector with fluorescent reporter. After transfection, positive cells are sorted out and for sequencing. With the results, we successfully identify hundreds known accurate active enhancer sequences as expected. Besides that, large amounts of unmarked regulatory elements (UREs) that might control gene expression without epigenetic features are also been spotted. In conclusion, MAE-Seq would be useful to refine enhancer sequences and precisely annotate the genome in eukaryotes.
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Project description:BACKGROUND:Enhancers are DNA regulatory elements that influence gene expression. There is substantial diversity in enhancers' activity patterns: some enhancers drive expression in a single cellular context, while others are active across many. Sequence characteristics, such as transcription factor (TF) binding motifs, influence the activity patterns of regulatory sequences; however, the regulatory logic through which specific sequences drive enhancer activity patterns is poorly understood. Recent analysis of Drosophila enhancers suggested that short dinucleotide repeat motifs (DRMs) are general enhancer sequence features that drive broad regulatory activity. However, it is not known whether the regulatory role of DRMs is conserved across species. RESULTS:We performed a comprehensive analysis of the relationship between short DNA sequence patterns, including DRMs, and human enhancer activity in 38,538 enhancers across 411 different contexts. In a machine-learning framework, the occurrence patterns of short sequence motifs accurately predicted broadly active human enhancers. However, DRMs alone were weakly predictive of broad enhancer activity in humans and showed different enrichment patterns than in Drosophila. In general, GC-rich sequence motifs were significantly associated with broad enhancer activity, and consistent with this enrichment, broadly active human TFs recognize GC-rich motifs. CONCLUSIONS:Our results reveal the importance of specific sequence motifs in broadly active human enhancers, demonstrate the lack of evolutionary conservation of the role of DRMs, and provide a computational framework for investigating the logic of enhancer sequences.
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