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Tytuł pozycji:

Identification of the human DPR core promoter element using machine learning.

Tytuł :
Identification of the human DPR core promoter element using machine learning.
Autorzy :
Vo Ngoc L; Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
Huang CY; Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
Cassidy CJ; Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
Medrano C; Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
Kadonaga JT; Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA. .
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Źródło :
Nature [Nature] 2020 Sep; Vol. 585 (7825), pp. 459-463. Date of Electronic Publication: 2020 Sep 09.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Język :
English
Imprint Name(s) :
Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.
MeSH Terms :
Support Vector Machine*
Transcription, Genetic*
Consensus Sequence/*genetics
Gene Expression Regulation/*genetics
Promoter Regions, Genetic/*genetics
RNA Polymerase II/*metabolism
Base Sequence ; Cells/metabolism ; Computer Simulation ; Datasets as Topic ; HeLa Cells ; High-Throughput Nucleotide Sequencing ; Humans ; Models, Genetic ; Mutagenesis ; TATA Box/genetics
Grant Information :
P30 CA023100 United States CA NCI NIH HHS; R35 GM118060 United States GM NIGMS NIH HHS; S10 OD026929 United States OD NIH HHS
Substance Nomenclature :
EC 2.7.7.- (RNA Polymerase II)
Entry Date(s) :
Date Created: 20200910 Date Completed: 20201021 Latest Revision: 20201021
Update Code :
20201023
PubMed Central ID :
PMC7501168
DOI :
10.1038/s41586-020-2689-7
PMID :
32908305
Czasopismo naukowe
The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription 1-5 , but the downstream core promoter in humans has been difficult to understand 1-3 . Here we analyse the human Pol II core promoter and use machine learning to generate predictive models for the downstream core promoter region (DPR) and the TATA box. We developed a method termed HARPE (high-throughput analysis of randomized promoter elements) to create hundreds of thousands of DPR (or TATA box) variants, each with known transcriptional strength. We then analysed the HARPE data by support vector regression (SVR) to provide comprehensive models for the sequence motifs, and found that the SVR-based approach is more effective than a consensus-based method for predicting transcriptional activity. These results show that the DPR is a functionally important core promoter element that is widely used in human promoters. Notably, there appears to be a duality between the DPR and the TATA box, as many promoters contain one or the other element. More broadly, these findings show that functional DNA motifs can be identified by machine learning analysis of a comprehensive set of sequence variants.

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