研究業績Publication

学術論文(2011年度〜)

  • A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage, Yada T, Taniguchi T, BMC Bioinform Suppl, (in submission).   [pdf]   [data]
  • Identification and analysis of short open reading frames (sORFs) in the initially annotated noncoding RNA LINC00493 from human cells, Yeasmin F, Imamachi N, Tanu T, Taniue K, Kawamura T, Yada T, Akimitsu N, J Biochem, 169, 421-434 (2021).
  • Dynamical robustness and its structural dependence in biological networks, Ichinose N, Kawashima T, Yada T, Wada H, J Theor Biol, 526, 110808 (2021).
  • Discrimination between dementia groups and healthy elderlies using scalp-recorded-EEG-based brain functional connectivity networks, Nishijima S, Yada T, Yamazaki T, Kuroiwa Y, Nakane M, Fujino K, Hirai T, Baba Y, Yamada S, Tsukiyama S, J Biomed Sci Eng, 13, 153-167 (2020).
  • Asymmetry in indegree and outdegree distributions of gene regulatory networks arising from dynamical robustness, Ichinose N, Yada T, Wada H, Phys Rev E, 97, 0062315 (2018).
  • Micropeptides encoded in transcripts previously identified as long noncoding RNAs: A new chapter in transcriptomics and proteomics, Yeasmin F, Yada T, Akimitsu N, Front Genet, 25 April (2018).
  • Identification of minimal p53 promoter region regulated by MALAT1 in human lung adenocarcinoma cells, Tano K, Onoguchi-Mizutani R, Yeasmin F, Uchiumi F, Suzuki Y, Yada T, Akimitsu N, Front Genet, 26 Mar, (2018).
  • A new computational method to predict transcriptional activity of a DNA sequence from diverse datasets of massively parallel reporter assays, Liu Y, Irie T, Yada T, Suzuki Y, Nucleic Acids Res, 45, e124, (2017).
  • Estimating optimal sparseness of developmental gene networks using a semi-quantitative model, Ichinose N, Yada T, Wada H, PLoS One, 12, e0176492, (2017).
  • Erratum to: General continuous-time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?, Ezawa K, BMC Bioinform, 17, 457, (2016).
  • General continuous-time Markov model of sequence evolution via insertions/deletions: local alignment probability computation, Ezawa K, BMC Bioinform, 17, 397, (2016).
  • General continuous-time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?, Ezawa K, BMC Bioinform, 17, 304, (2016).
  • Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map, Ezawa K, BMC Bioinform, 17, 133, (2016).
  • Analysis of RNA decay factor mediated RNA stability contributions on RNA abundance, Maekawa S, Imamachi N, Irie T, Tani H, Matsumoto K, Mizutani R, Imamura K, Kakeda M, Yada T, Sugano S, Suzuki Y, Akimitsu N, BMC Genomics, 16, 154, (2015).
  • Tetrahedral gray code for visualization of genome information, Ichinose N, Yada T, Gotoh O, PLoS One, 9, e86133, (2014).
  • Long Noncoding RNA NEAT1-Dependent SFPQ Relocation from Promoter Region to Paraspeckle Mediates IL8 Expression upon Immune Stimuli, Imamura K, Imamachi N, Akizuki G, Kumakura M, Kawaguchi A, Nagata K, Kato A, Kawaguchi Y, Sato H, Yoneda M, Kai C, Yada T, Suzuki Y, Yamada T, Ozawa T, Kaneki K, Inoue T, Kobayashi M, Kodama T, Wada Y, Sekimizu K, Akimitsu N, Mol Cell, 53, 393-406, (2014).
  • miRNA-target prediction based on transcriptional regulation, Fujiwara T, Yada T, BMC Genomics, 14(Suppl 2), S3, (2013).
  • Identification of hundreds of novel UPF1 target transcripts by direct determining whole transcriptome stability in mammalian cells, Tani H, Imamachi N, Salam KA. Mizutani,R, Ijiri K, Irie T, Yada T, Suzuki Y, Akimitsu N, RNA Biol., 9, 1370-1379, (2012).
  • Large-scale motif discovery using DNA Gray code and equiprobable oligomers, Ichinose N, Yada T, Gotoh O, Bioinform., 28, 25-31, (2012).
  • Linear regression models predicting strength of transcriptional activity of promoters, Yada T, Yoshida K, Morita M, Taniguchi T, Irie T, Suzuki Y, Genome Inform., 25, 53-60, (2011).
  • Predicting promoter activities of primary human DNA sequences, Irie T, Park SJ, Yamashita R, Seki M, Yada T, Sugano S, Nakai K, Suzuki Y, Nucl. Acids Res., 39, e75, (2011).

総説・解説(2011年度〜)

  • Genome sequence alignment, Yada T, Encyclopedia of Bioinformatics and Computational Biology (Gaeta B, Nakai K, ed.), Elsevier, 268-283 (2019).
  • 遺伝子発見, 矢田哲士, 人工知能学大事典(人工知能学会 編), 共立出版, 964-966 (2017).
  • ゲノミクス, 矢田哲士, バイオインフォマティクス(A.ポランスキ, M.キンメル 著: 後藤修 訳), 丸善出版, 217-266 (2012).
  • Hegma: 大規模DNA配列を対象としたモチーフ発見ツール, 市瀬夏洋, 矢田哲士, 後藤修, 日本バイオインフォマティクス学会ニュースレーター, 24, 5-6 (2012).

講演(2011年度〜)

  • A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage, Yada T, GIW 2022. Dec 12-14, 2022. National Cheng-Kung University (NCKU), Taiwan.
  • 遺伝子de novo誕生のよもやま話, 矢田哲士, 配列解析シンポジウム: 36 years since Smith-Waterman-Gotoh, 2018年3月28日. 産業技術総合研究所, 東京.
  • A putative scenario for de novo gene birth in Saccharomyces cerevisiae genome, Yada T, BIT 2016, Mar 3-4, 2016. Yang-Ming University, Taiwan.
  • ゲノム大規模データを解析する−転写制御領域の解読と設計−, 矢田哲士, 新生命科学分野開拓とスーパーコンピューター「京」. 2014年9月16日. 九州大学, 福岡.
  • Computer aided design of human promoter sequences based on massively parallel reporter assay data, Yada T, JSMB/SMB 2014. Jul 28-Aug 1, 2014. Osaka International Convention Center, Osaka.
  • 転写因子の結合と転写の関係を明らかにする試み, 矢田哲士, 新学術領域研究「転写サイクル」第2回トレーニングワークショップ. 2014年3月8日. 九州工業大学, 福岡.
  • プロモーター配列の合理的な設計に挑む, 矢田哲士, 情報処理学会第37回バイオ情報学研究会. 2014年3月5日. 九州工業大学, 福岡.
  • Deciphering regulatory code underlying promoter sequences, Yada T, The 2nd BMIRC International Symposium on Advances in Bioinformatics and Medical Engineering. Jan 29-30, 2014. Center of Iizuka Research and Development, Fukuoka.
  • 転写制御コードの多様性に挑む, 矢田哲士, ゲノム多様性のデータ利用. 2013年12月20-21日. 統計数理研究所, 東京.