Elucidation of splicing-code with RNA-specialized machine learning system toward overcoming hereditary diseases having splicing misregulations

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K07310
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 48040:Medical biochemistry-related
• Research Institution: Kindai University (2022); Kyoto University (2020-2021)
• Principal Investigator: IIDA Kei 近畿大学, 理工学部, 講師 (00387961)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: RNAスプライシング / 人工知能 / ケミカルバイオロジー

Outline of Final Research Achievements

Due to progress in research on genetic diseases and development of sequencing technology, there are increasing reports of cases in which mutations occurring in introns cause diseases. We still do not have a systematic understanding of how mutations in introns affect mature mRNAs. In this study, we aimed to clarify the details of splicing control by applying artificial intelligence (AI) technology, which has been developing remarkably in recent years. We have constructed an Explainable AI system for SpliceAI developed by llumina, Inc., enabling the identification of nucleotide sequences involved in splicing control in individual genes and the elucidation of disease-related splicing control based on these.

Free Research Field

情報生物学

Academic Significance and Societal Importance of the Research Achievements

スプライシングの詳細を明らかにするExplainable AIの開発により、COVID-19の重症化に関与するスプライシング制御の解明や、これに基づく、感染率低減法の提案につなげることができた。