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2019 Fiscal Year Final Research Report

Development of transcriptional regulatory network prediction methods in morphogenesis

Research Project

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Project/Area Number 17K15132
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Developmental biology
Research InstitutionInstitute of Physical and Chemical Research

Principal Investigator

Onimaru Koh  国立研究開発法人理化学研究所, 生命機能科学研究センター, 基礎科学特別研究員 (30787065)

Project Period (FY) 2017-04-01 – 2020-03-31
Keywords深層学習 / 形態形成 / 転写制御配列
Outline of Final Research Achievements

This project was aimed at development of transcriptional regulatory prediction methods in morphogenesis by applying deep learning. We set the following two tasks to achieve this goal: a) the genome-wide identification of morphogenic transcriptional enhancers using mouse limb buds; b) developing deep learning methods to analyze enhancer sequences and infer gene regulatory networks. We successfully determined limb-associated morphogenic enhancers and analyzed the characteristics of these sequences. Moreover, we developed a deep learning-based regulatory sequence classifier that outperformed previous studies. This software can extract information that is critical for transcriptional regulation from genomic sequences. As an output of this project, we have published one peer-reviewed original research paper and two original research papers as preprints and released the developed program as an open-source software.

Free Research Field

発生生物学

Academic Significance and Societal Importance of the Research Achievements

本研究成果における学術的な意義は、形態形成における転写制御配列について新たな特徴、傾向を発見し、転写制御と形態の多様性について理解を深めることに貢献したことにある。また、深層学習を用いた新たなゲノム配列の解析方法の提案が出来、ゲノム配列と生物の形態を結びつける研究が発展する上で礎となることが期待される。社会貢献としては、本研究は、ヒトの個々のゲノム配列に対する新たな解釈を行う上で、基礎的な知見が得られ、技術開発のさきがけとなる成果が得られたと考えている。

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Published: 2021-02-19  

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