본 분자신경생화학 실험실에서는 전장유전체 연관분석(GWAS)을 통한 인간의 질환과 관련된 다양한 유전자의 변화 및 비번역 RNA의 역할과 기능 해명을 하고 있습니다.

   주재열(Jae-Yeol Joo)
   Associate Professor, College of Pharmacy, Hanyang University

  - EDUCATION & EXPERIENCES
    2012 : Ph. D. The University of Tokyo, Graduate School of Medicine, Tokyo, Japan
    2005 : M.S. Hanyang University, College of Medicine, Seoul, Korea
    2003 : B.S Hanyang University, Life Science, Seoul, Korea
    2022.03 - Present. : Associate Professor, College of Pharmacy, Hanyang University
    2016.12 - 2022.02. : Principal Investigator, Korea Brain Research Institute
    2012 - 2016.11. : Postdoctoral Research Fellow, The University of Texas Southwestern Medical Center
    2006 - 2007. : Researcher, Graduate School of Medicine, The University of Tokyo

  I. Elucidation of human brain diseases through RNA and gene expression   

- Therapeutic strategies designed for brain disorder diagnosis from lncRNAs

It is thought that lncRNAs are involved in modulation of gene expression, and their canonical functions start at a genetic locus. Considering that cells are constantly subject to transcriptional regulation that is affected by various lncRNAs studies of lncRNA pathways in mammals have been hampered by a lack of informative genetic systems.

In a significant advancement using a genome-wide association study (GWAS), a lncRNA-associated transcriptional regulation system has provided molecular insights into the biochemical steps involved in promotion of mRNA synthesis. As many lncRNAs have other functions in addition to transcriptional regulation, lncRNAs are tightly linked to many other biological processes. In addition to uncovering the molecular mechanisms that affect neural activity, advancements in genomic sequencing analysis have led to the discovery of various new biomarkers of brain disorders (BDs).

  II. Application of an artificial intelligence (AI) in human diseases with GWAS     

- Finding novel therapeutic target, and cryptic variation for human diseases through ultra-long read sequencing

In the past decade, it has become apparent that high-throughput analysis has seen considerable progress in human genomics. In addition, deep-learning based computational technologies with genetic information have applicated to the comparison and prediction of alternative splicing variants between health and disease. GWAS have been revealed diversity of genomic events, there is an increasing number of cryptic splice variants. With the advent of genome sequencing technology, large-scale genomic data-based machine learning tools have been used to predict and identify somatic inactivation or negative dominant expression of target genes in diseases. Therefore, deep-learning methods have helped to reveal distinct roles that link somatic variations to the function of proteins, especially those affected by sex, aging, and environmental factors.

Disease-associated clinical approach through the prediction of alternative splicing variants from human genomic sequence with the deep learning-based SpliceAI