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

  • Mirage: Estimation of ancestral gene-copy numbers by considering different evolutionary patterns among gene families

    Tsukasa Fukunaga, Wataru Iwasaki

    Bioinformatics Advances    2021年07月

     概要を見る

    <title>Abstract</title>
    <sec>
    <title>Motivation</title>
    Reconstruction of gene copy number evolution is an essential approach for understanding how complex biological systems have been organized. Although various models have been proposed for gene copy number evolution, existing evolutionary models have not appropriately addressed the fact that different gene families can have very different gene gain/loss rates.


    </sec>
    <sec>
    <title>Results</title>
    In this study, we developed Mirage (MIxtuRe model for Ancestral Genome Estimation), which allows different gene families to have flexible gene gain/loss rates. Mirage can use three models for formulating heterogeneous evolution among gene families: the discretized Γ model, PDF model, and PM model. Simulation analysis showed that Mirage can accurately estimate heterogeneous gene gain/loss rates and reconstruct gene content evolutionary history. Application to empirical datasets demonstrated that the PM model fits genome data from various taxonomic groups better than the other heterogeneous models. Using Mirage, we revealed that metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated.


    </sec>
    <sec>
    <title>Availability</title>
    The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage.


    </sec>
    <sec>
    <title>Supplementary information</title>
    Supplementary data are available at Bioinformatics Advances online.


    </sec>

    DOI

  • Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

    Shion Hosoda, Tsukasa Fukunaga, Michiaki Hamada

    Bioinformatics (Oxford, England)   37 ( Suppl_1 ) i16-i24  2021年07月  [国際誌]

     概要を見る

    MOTIVATION: Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. RESULTS: In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. AVAILABILITY AND IMPLEMENTATION: The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    DOI PubMed

  • Representation learning applications in biological sequence analysis.

    Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada, Natsuki Iwano, Shunsuke Sumi, Shion Hosoda, Shitao Zhao, Tsukasa Fukunaga, Michiaki Hamada

    Computational and structural biotechnology journal   19   3198 - 3208  2021年  [国際誌]

     概要を見る

    Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

    DOI PubMed

  • 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

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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