Predicting promoter activities of primary human DNA sequences

Takuma Irie, Sung Joon Park, Riu Yamashita, Masahide Seki, Tetsushi Yada, Sumio Sugano, Kenta Nakai, Yutaka Suzuki

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

We developed a computer program that can predict the intrinsic promoter activities of primary human DNA sequences. We observed promoter activity using a quantitative luciferase assay and generated a prediction model using multiple linear regression. Our program achieved a prediction accuracy correlation coefficient of 0.87 between the predicted and observed promoter activities. We evaluated the prediction accuracy of the program using massive sequencing analysis of transcriptional start sites in vivo. We found that it is still difficult to predict transcript levels in a strictly quantitative manner in vivo; however, it was possible to select active promoters in a given cell from the other silent promoters. Using this program, we analyzed the transcriptional landscape of the entire human genome. We demonstrate that many human genomic regions have potential promoter activity, and the expression of some previously uncharacterized putatively non-protein-coding transcripts can be explained by our prediction model. Furthermore, we found that nucleosomes occasionally formed open chromatin structures with RNA polymerase II recruitment where the program predicted significant promoter activities, although no transcripts were observed.

Original languageEnglish
Pages (from-to)e75
JournalNucleic acids research
Volume39
Issue number11
DOIs
Publication statusPublished - 2011 Jun

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'Predicting promoter activities of primary human DNA sequences'. Together they form a unique fingerprint.

Cite this