福永 津嵩 (フクナガ ツカサ)

写真a

所属

附属機関・学校 高等研究所

職名

講師(任期付)

学歴 【 表示 / 非表示

  • 2011年04月
    -
    2016年03月

    東京大学大学院   新領域創成科学研究科   メディカル情報生命専攻  

  • 2007年04月
    -
    2011年03月

    東京大学   理学部   生物情報科学科  

学位 【 表示 / 非表示

  • 東京大学   博士(科学)

経歴 【 表示 / 非表示

  • 2021年04月
    -
    継続中

    早稲田大学   高等研究所   講師

  • 2017年10月
    -
    2021年03月

    早稲田大学   理工学術院総合研究所   招聘研究員

  • 2017年10月
    -
    2021年03月

    東京大学   情報理工学系研究科コンピュータ科学専攻   助教

  • 2018年02月
    -
    2019年03月

    大阪大学   医学系研究科   招聘研究員

  • 2016年04月
    -
    2017年09月

    日本学術振興会   特別研究員(PD)

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所属学協会 【 表示 / 非表示

  •  
     
     

    日本バイオインフォマティクス学会

 

研究分野 【 表示 / 非表示

  • システムゲノム科学

  • ゲノム生物学

  • 生命、健康、医療情報学

研究キーワード 【 表示 / 非表示

  • データマイニング

  • 機械学習

  • RNA

  • バイオインフォマティクス

  • 表現型

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論文 【 表示 / 非表示

  • Novel metric for hyperbolic phylogenetic tree embeddings.

    Hirotaka Matsumoto, Takahiro Mimori, Tsukasa Fukunaga

    Biology methods & protocols   6 ( 1 ) bpab006  2021年  [査読有り]  [国際誌]

     概要を見る

    Advances in experimental technologies, such as DNA sequencing, have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. Furthermore, we proposed a novel approach based on our metric to integrate multiple trees for analyzing tree nodes or imputing missing distances. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods.

    DOI PubMed

  • MotiMul: A significant discriminative sequence motif discovery algorithm with multiple testing correction

    Koichi Mori, Haruka Ozaki, Tsukasa Fukunaga

    Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020     186 - 193  2020年12月  [査読有り]

     概要を見る

    Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-l error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarone's correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarone's correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://github.com/ko-ichimo-ri/MotiMul.

    DOI

  • Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

    Shion Hosoda, Suguru Nishijima, Tsukasa Fukunaga, Masahira Hattori, Michiaki Hamada

    Microbiome   8 ( 1 ) 95 - 95  2020年06月  [査読有り]  [国際誌]

     概要を見る

    Background: The human gut microbiome has been suggested to affect human health and thus has received considerable attention. To clarify the structure of the human gut microbiome, clustering methods are frequently applied to human gut taxonomic profiles. Enterotypes, i.e., clusters of individuals with similar microbiome composition, are well-studied and characterized. However, only a few detailed studies on assemblages, i.e., clusters of co-occurring bacterial taxa, have been conducted. Particularly, the relationship between the enterotype and assemblage is not well-understood. Results: In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method. We applied LDA to a large-scale human gut metagenome dataset and found that a 4-assemblage LDA model could represent relationships between enterotypes and assemblages with high interpretability. This model indicated that each individual tends to have several assemblages, three of which corresponded to the three classically recognized enterotypes. Conversely, the fourth assemblage corresponded to no enterotypes and emerged in all enterotypes. Interestingly, the dominant genera of this assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Indeed, the fourth assemblage significantly positively correlated with three butyrate-producing functions. Conclusions: We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA. The present study revealed that there is an enterotype-independent assemblage. [MediaObject not available: see fulltext.]

    DOI PubMed

  • Logicome profiler: Exhaustive detection of statistically significant logic relationships from comparative omics data

    Tsukasa Fukunaga, Wataru Iwasaki

    PLoS ONE   15 ( 5 ) e0232106  2020年05月  [査読有り]  [国際誌]

     概要を見る

    Logic relationship analysis is a data mining method that comprehensively detects item triplets that satisfy logic relationships from a binary matrix dataset, such as an ortholog table in comparative genomics. Thanks to recent technological advancements, many binary matrix datasets are now being produced in genomics, transcriptomics, epigenomics, metagenomics, and many other fields for comparative purposes. However, regardless of presumed interpretability and importance of logic relationships, existing data mining methods are not based on the framework of statistical hypothesis testing. That means, the type-1 and type-2 error rates are neither controlled nor estimated. Here, we developed Logicome Profiler, which exhaustively detects statistically significant triplet logic relationships from a binary matrix dataset (Logicome means ome of logics). To test all item triplets in a dataset while avoiding false positives, Logicome Profiler adjusts a significance level by the Bonferroni or Benjamini-Yekutieli method for the multiple testing correction. Its application to an ocean metagenomic dataset showed that Logicome Profiler can effectively detect statistically significant triplet logic relationships among environmental microbes and genes, which include those among urea transporter, urease, and photosynthesis-related genes. Beyond omics data analysis, Logicome Profiler is applicable to various binary matrix datasets in general for finding significant triplet logic relationships. The source code is available at https://github.com/fukunagatsu/LogicomeProfiler.

    DOI PubMed

  • Targeting the TR4 nuclear receptor-mediated lncTASR/AXL signaling with tretinoin increases the sunitinib sensitivity to better suppress the RCC progression

    Hangchuan Shi, Yin Sun, Miao He, Xiong Yang, Michiaki Hamada, Tsukasa Fukunaga, Xiaoping Zhang, Chawnshang Chang

    Oncogene   39 ( 3 ) 530 - 545  2020年01月  [査読有り]  [国際誌]

     概要を見る

    Renal cell carcinoma (RCC) is one of the most lethal urological tumors. Using sunitinib to improve the survival has become the first-line therapy for metastatic RCC patients. However, the occurrence of sunitinib resistance in the clinical application has curtailed its efficacy. Here we found TR4 nuclear receptor might alter the sunitinib resistance to RCC via altering the TR4/lncTASR/AXL signaling. Mechanism dissection revealed that TR4 could modulate lncTASR (ENST00000600671.1) expression via transcriptional regulation, which might then increase AXL protein expression via enhancing the stability of AXL mRNA to increase the sunitinib resistance in RCC. Human clinical surveys also linked the expression of TR4, lncTASR, and AXL to the RCC survival, and results from multiple RCC cell lines revealed that targeting this newly identified TR4-mediated signaling with small molecules, including tretinoin, metformin, or TR4-shRNAs, all led to increase the sunitinib sensitivity to better suppress the RCC progression, and our preclinical study using the in vivo mouse model further proved tretinoin had a better synergistic effect to increase sunitinib sensitivity to suppress RCC progression. Future successful clinical trials may help in the development of a novel therapy to better suppress the RCC progression.

    DOI PubMed

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共同研究・競争的資金等の研究課題 【 表示 / 非表示

  • リピート要素のde novo発見に基づく長鎖ノンコーディングRNAの機能の解明

    基盤研究(A)

    研究期間:

    2020年04月
    -
    2023年03月
     

    浜田 道昭, 小野口 真広, 福永 津嵩

  • 逆イジングモデル法に基づく機能未知な微生物遺伝子の機能推定

    新学術領域研究(研究領域提案型)

    研究期間:

    2020年04月
    -
    2022年03月
     

    福永 津嵩

  • 統計的論理関係解析法に基づく機能未知遺伝子の機能推定

    若手研究

    研究期間:

    2019年04月
    -
    2022年03月
     

    福永 津嵩

  • lncRNA-mRNAの相互作用ネットワークに基づくlncRNAの機能推定

    研究期間:

    2017年04月
    -
    2019年03月
     

    福永 津嵩

    担当区分: 研究代表者

  • Computational Ethologyで解き明かす動物の群れ形成メカニズム

    研究期間:

    2016年04月
    -
    2019年03月
     

    福永 津嵩

    担当区分: 研究代表者

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現在担当している科目 【 表示 / 非表示