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  1. AU="Ishitsuka, Yasuhiro"
  2. AU=Sims Jonathan T AU=Sims Jonathan T
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  1. Article ; Online: Emergent centrality in rank-based supplanting process.

    Shimomura, Kenji / Ishitsuka, Yasuhiro / Ohta, Hiroki

    Physical review. E

    2023  Volume 107, Issue 3-1, Page(s) 34114

    Abstract: We propose a stochastic process of interacting many agents, which is inspired by rank-based supplanting dynamics commonly observed in a group of Japanese macaques. In order to characterize the breaking of permutation symmetry with respect to agents' rank ...

    Abstract We propose a stochastic process of interacting many agents, which is inspired by rank-based supplanting dynamics commonly observed in a group of Japanese macaques. In order to characterize the breaking of permutation symmetry with respect to agents' rank in the stochastic process, we introduce a rank-dependent quantity, overlap centrality, which quantifies how often a given agent overlaps with the other agents. We give a sufficient condition in a wide class of the models such that overlap centrality shows perfect correlation in terms of the agents' rank in the zero-supplanting limit. We also discuss a singularity of the correlation in the case of interaction induced by a Potts energy.
    Language English
    Publishing date 2023-04-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.107.034114
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells.

    Tanaka, Azusa / Ishitsuka, Yasuhiro / Ohta, Hiroki / Fujimoto, Akihiro / Yasunaga, Jun-Ichirou / Matsuoka, Masao

    PLoS computational biology

    2020  Volume 16, Issue 11, Page(s) e1008422

    Abstract: The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a ... ...

    Abstract The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.
    MeSH term(s) Algorithms ; Bone Marrow Cells/metabolism ; Chromatin/metabolism ; Cluster Analysis ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Leukemia/genetics ; Leukemia/pathology
    Chemical Substances Chromatin
    Language English
    Publishing date 2020-11-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1008422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Integrative analysis of ATAC-seq and RNA-seq for cells infected by human T-cell leukemia virus type 1

    Tanaka, Azusa / Ishitsuka, Yasuhiro / Ohta, Hiroki / Takenouchi, Norihiro / Nakagawa, Masanori / Koh, Ki-Ryang / Onishi, Chiho / Tanaka, Hiromitsu / Fujimoto, Akihiro / Yasunaga, Jun-ichirou / Matsuoka, Masao

    2023  

    Abstract: Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy (HAM) after a long latent period in a fraction of infected individuals. These HTLV-1-infected cells typically have phenotypes similar to that ... ...

    Abstract Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy (HAM) after a long latent period in a fraction of infected individuals. These HTLV-1-infected cells typically have phenotypes similar to that of CD4${^+}$ T cells, but the cell status is not well understood. To extract the inherent information of HTLV-1-infected CD4$^+$ cells, we integratively analyzed the ATAC-seq and RNA-seq data of infected cells. Compared to CD4${^+}$ T cells from healthy donors, we found anomalous chromatin accessibility in HTLV-1-infected CD4${^+}$ cells derived from ATL cases in terms of location and sample-to-sample fluctuations in open chromatin regions. Further, by focusing on systematically selected genes near the open chromatin regions, all the gene expressions in ATL cases were found to be distinct from those of healthy CD4$^+$ T cells. Based on a further analysis of chromatin accessibility, we detected TLL1 (Tolloid Like 1) as one of the key genes that exhibit unique gene expressions in ATL cases. A luciferase assay indicated that TLL1 has a strong regulatory effect on TGF-$\beta$. Overall, this study provides results about the status of HTLV-1 infected cells, which are qualitatively consistent across the different scales of chromatin accessibility, transcription, and immunophenotype.

    Comment: 23 pages, 13 figures
    Keywords Quantitative Biology - Genomics ; Physics - Data Analysis ; Statistics and Probability ; Quantitative Biology - Quantitative Methods
    Subject code 570
    Publishing date 2023-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells

    Tanaka, Azusa / Ishitsuka, Yasuhiro / Ohta, Hiroki / Fujimoto, Akihiro / Yasunaga, Jun-ichirou / Matsuoka, Masao

    2019  

    Abstract: The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a ... ...

    Abstract The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of $1$s and $0$s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.

    Comment: 24 pages, 17 figures
    Keywords Quantitative Biology - Genomics ; Condensed Matter - Statistical Mechanics ; Quantitative Biology - Quantitative Methods
    Publishing date 2019-12-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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