Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 주재열 | - |
dc.date.accessioned | 2022-08-11T01:42:17Z | - |
dc.date.available | 2022-08-11T01:42:17Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, v. 118, NO 3, Page. 1-9 | en_US |
dc.identifier.issn | 00278424 | - |
dc.identifier.uri | https://www.pnas.org/doi/full/10.1073/pnas.2011250118 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172300 | - |
dc.description.abstract | Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer's disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLC.1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLC.1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLC.1 gene, and one of these completely matched with an SNV in exon 27 of PLC.1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLC.1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction. | en_US |
dc.description.sponsorship | We thank Dr. S.-W. Lee for assistance with bioinfor-matics analysis. This work was supported by KBRI Basic research programthrough KBRI funded by the Ministry of Science and ICT (Grant 20-BR-02-13), and Basic Science Research Program through the National ResearchFoundation of Korea funded by the Ministry of Education (Grants2019R1F1A1059595 and 2017R1D1A1B03030741). Figures were createdwithBiorender.com. | en_US |
dc.language.iso | en | en_US |
dc.publisher | NATL ACAD SCIENCES | en_US |
dc.subject | Alzheimer's disease | en_US |
dc.subject | deep learning | en_US |
dc.subject | PLC gamma 1 | en_US |
dc.subject | single-nucleotide variation | en_US |
dc.title | Prediction of Alzheimer's disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 118 | - |
dc.identifier.doi | 10.1073/pnas.2011250118 | - |
dc.relation.page | 1-9 | - |
dc.relation.journal | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | - |
dc.contributor.googleauthor | Kim, Sung-Hyun | - |
dc.contributor.googleauthor | Yang, Sumin | - |
dc.contributor.googleauthor | Lim, Key-Hwan | - |
dc.contributor.googleauthor | Ko, Euiseng | - |
dc.contributor.googleauthor | Jang, Hyun-Jun | - |
dc.contributor.googleauthor | Kang, Mingon | - |
dc.contributor.googleauthor | Suh, Pann-Ghill | - |
dc.contributor.googleauthor | Joo, Jae-Yeol | - |
dc.relation.code | 2021001544 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF PHARMACY[E] | - |
dc.sector.department | DEPARTMENT OF PHARMACY | - |
dc.identifier.pid | joojy | - |
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