FUKUNAGA, Tsukasa

写真a

Affiliation

Affiliated organization, Waseda Institute for Advanced Study

Job title

Assistant Professor(non-tenure-track)

Concurrent Post 【 display / non-display

  • Faculty of Science and Engineering   School of Advanced Science and Engineering

Education 【 display / non-display

  • 2011.04
    -
    2016.03

    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  

  • 2007.04
    -
    2011.03

    The University of Tokyo   Faculty of Science   Undergraduate Program for Bioinformatics and Systems Biology  

Degree 【 display / non-display

  • The University of Tokyo   Ph. D.

Research Experience 【 display / non-display

  • 2021.04
    -
    Now

    Waseda University

  • 2017.10
    -
    2021.03

    Waseda University   Research Institute for Science and Engineering

  • 2017.10
    -
    2021.03

    The University of Tokyo

  • 2018.02
    -
    2019.03

    Osaka University

  • 2016.04
    -
    2017.09

    日本学術振興会   特別研究員(PD)

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Professional Memberships 【 display / non-display

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    JAPANESE SOCIETY FOR BIOINFORMATICS

 

Research Areas 【 display / non-display

  • System genome science

  • Genome biology

  • Life, health and medical informatics

Research Interests 【 display / non-display

  • 表現型

  • 遺伝子機能推定

  • ゲノム進化

  • データマイニング

  • 機械学習

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Papers 【 display / non-display

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

    Tsukasa Fukunaga, Wataru Iwasaki

    Bioinformatics Advances    2021.07

     View Summary

    <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  2021.07  [International journal]

     View Summary

    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  [International journal]

     View Summary

    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  [Refereed]  [International journal]

     View Summary

    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  2020.12  [Refereed]

     View Summary

    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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Research Projects 【 display / non-display

  • 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)

    Project Year :

    2020.04
    -
    2023.03
     

    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)

    Project Year :

    2020.04
    -
    2022.03
     

    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

    Project Year :

    2019.04
    -
    2022.03
     

    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

    Project Year :

    2017.04
    -
    2019.03
     

    福永 津嵩

    Authorship: Principal investigator

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

    Project Year :

    2016.04
    -
    2019.03
     

    福永 津嵩

    Authorship: Principal investigator

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Syllabus 【 display / non-display