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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
Published on: June 28, 2018
Predicting promoter activities of primary human DNA sequences
Takuma Irie1, Sung-Joon Park, Riu Yamashita
1Department of Medical Genome Sciences, Graduate School of Frontier Sciences, the University of Tokyo, 5-1-5 Kashiwanoha, Kashiwashi, Chiba 277-8562, Japan.
Nucleic Acids Research
|April 14, 2011
Summary
Researchers created a computational tool to predict human DNA promoter activity, achieving 0.87 accuracy. This tool helps identify active promoters in the genome, even for uncharacterized transcripts.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Promoters are crucial DNA sequences regulating gene expression.
- Predicting promoter activity computationally is challenging but essential for understanding gene regulation.
Purpose of the Study:
- To develop a computational program for predicting intrinsic promoter activities of human DNA sequences.
- To assess the program's accuracy in vitro and in vivo.
- To analyze the human genome's transcriptional landscape using the developed program.
Main Methods:
- Quantitative luciferase assays to measure promoter activity.
- Multiple linear regression to build a prediction model.
- Massive sequencing analysis of transcriptional start sites for in vivo validation.
Main Results:
- The program achieved a prediction accuracy correlation coefficient of 0.87.
- While precise in vivo transcript level prediction remains difficult, active promoters can be distinguished from silent ones.
- Analysis revealed widespread potential promoter activity across the human genome, explaining some non-coding RNA expression.
- Open chromatin structures with RNA polymerase II recruitment were observed at predicted active promoter sites, even without observed transcripts.
Conclusions:
- The developed computational tool accurately predicts human DNA promoter activity.
- The tool aids in identifying functional promoters within the vast human genome.
- Findings suggest extensive, yet uncharacterized, transcriptional potential in the human genome.
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