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Algorithms that organisms search genes de novo

Research Project

Project/Area Number 18H03335
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Review Section Basic Section 62010:Life, health and medical informatics-related
Research InstitutionKyushu Institute of Technology

Principal Investigator

Yada Tetsushi  九州工業大学, 大学院情報工学研究院, 教授 (10322728)

Project Period (FY) 2018-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥17,160,000 (Direct Cost: ¥13,200,000、Indirect Cost: ¥3,960,000)
Fiscal Year 2022: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2021: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2020: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2019: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Keywords遺伝子のde novo誕生 / 生物の遺伝子探索アルゴリズム / バイオインフォマティクス解析
Outline of Final Research Achievements

De novo gene birth is the process that new genes arise from non-genic DNA sequences by accumulating mutations. Until recently, this process was thought to occur almost never, but advances in genome research have revealed that it is a far more common process. On the other hand, this process can be viewed as an organism's search for new gene sequences. Then, even the de novo birth of a short gene consisting of only 90 nucleotides would involve the exploration of a vast state space of more than 4 to the 90th power. Here, we revealed the full extent of the algorithm of the organism that efficiently searches for a number of genes from the vast state space by applying bioinformatics analysis of genome data.

Academic Significance and Societal Importance of the Research Achievements

出芽酵母に至る系統でのバイオインフォマティクス解析により、遺伝子のde novo誕生の典型的な過程、すなわち、GCに富む領域に中立な突然変異が蓄積することで、まず、候補遺伝子領域長が伸長し、次に、翻訳シグナル配列を獲得する、を明らかにした。そして、候補遺伝子領域長を伸長する中立な突然変異の数が翻訳シグナル配列を獲得するその数より多いことから、遺伝子のde novo誕生が機会的な過程であることを見いだした。また、自然言語処理の分野で発展した様々な技術を応用することで、遺伝子領域長に関係なく、それらのタンパク質コーディング性を推定するdeep learningモデルを初めて開発した。

Report

(6 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Annual Research Report
  • 2020 Annual Research Report
  • 2019 Annual Research Report
  • 2018 Annual Research Report
  • Research Products

    (7 results)

All 2023 2022 2021 2019

All Journal Article (3 results) (of which Peer Reviewed: 3 results) Presentation (4 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage2023

    • Author(s)
      Yada T, Taniguchi T
    • Journal Title

      BMC Bioinformatics

      Volume: -

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Dynamical robustness and its structural dependence in biological networks2021

    • Author(s)
      Ichinose N, Kawashima T, Yada T, Wada H
    • Journal Title

      J Theor Biol

      Volume: 526 Pages: 110808-110808

    • DOI

      10.1016/j.jtbi.2021.110808

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Genome sequence alignment2019

    • Author(s)
      Yada T
    • Journal Title

      Encyclopedia of Bioinformatics and Computational Biology (Gaeta B, Nakai K, ed.)

      Volume: 2 Pages: 268-283

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Presentation] A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage2022

    • Author(s)
      Yada T
    • Organizer
      GIW XXXI/ISCB-Asia V
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 低次のk-merの出現頻度を用いてRNA配列中のコーディングsmORFを発見する2021

    • Author(s)
      矢田哲士, 佐藤巽
    • Organizer
      第44回日本分子生物学会年会
    • Related Report
      2021 Annual Research Report
  • [Presentation] Prediction of human protein-coding smORFs using k-mer based machine learning2021

    • Author(s)
      Sato T, Yada T
    • Organizer
      2021年日本バイオインフォマティクス学会年会
    • Related Report
      2021 Annual Research Report
  • [Presentation] Does existance of intron increase birth rate of de novo gene?2019

    • Author(s)
      Keisuke Ando, Tetsushi Yada
    • Organizer
      2019年日本バイオインフォマティクス学会年会・第8回生命医薬情報学連合大会(IIBMP2019)
    • Related Report
      2019 Annual Research Report

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Published: 2018-04-23   Modified: 2024-01-30  

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