Algorithms that organisms search genes de novo

• Project/Area Number: 18H03335
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: Kyushu 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: ¥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。

Research Products

• [Journal Article] A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage (2023)
  • Author(s): Yada T, Taniguchi T
  • Journal Title: BMC Bioinformatics Volume: -
  • Peer Reviewed
• [Journal Article] Dynamical robustness and its structural dependence in biological networks (2021)
  • 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
  • Peer Reviewed
• [Journal Article] Genome sequence alignment (2019)
  • Author(s): Yada T
  • Journal Title: Encyclopedia of Bioinformatics and Computational Biology (Gaeta B, Nakai K, ed.) Volume: 2 Pages: 268-283
  • Peer Reviewed
• [Presentation] A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage (2022)
  • Author(s): Yada T
  • Organizer: GIW XXXI/ISCB-Asia V
  • 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
• [Presentation] Prediction of human protein-coding smORFs using k-mer based machine learning (2021)
  • 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
• [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）