Author

K. Yura

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Research Focus:gene regulatory network inference· 2 Assay:scRNA-seq· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

H. Takeyama· 2 shared K. Ide· 2 shared M. Hosokawa· 2 shared M. Kogawa· 2 shared R. Miyaoka· 2 shared Y. Nishikawa· 2 shared D. Yamada· 1 shared F. Hemmerling· 1 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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2 shared papersMicrobiology

H. Takeyama

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Targeted single-cell genomics reveals novel host adaptation strategies of the symbiotic bacteria Endozoicomonas in Acropora tenuis coral

K. Ide, Y. Nishikawa, T. Maruyama, Y. Tsukada, M. Kogawa, H. Takeda, R. Wagatsuma, R. Miyaoka, Y. Nakano, K. Kinjo, M. Ito, M. Hosokawa, K. Yura, S. Suda, H. Takeyama

This study uses single-cell genomics to analyze Endozoicomonas bacteria in Acropora tenuis corals, revealing host-adaptive genes that may enhance coral stress tolerance.

bioRxiv · Apr 22, 2022Microbiology

Single-cell metabolite detection and genomics reveals uncultivated talented producer

M. Kogawa, R. Miyaoka, F. Hemmerling, M. Ando, K. Yura, K. Ide, Y. Nishikawa, M. Hosokawa, Y. Ise, J. K. B. Cahn, K. Takada, S. Matsunaga, T. Mori, J. Piel, H. Takeyama

This study identifies an uncultivated bacterium as a producer of antifungal metabolites using Raman microscopy and single-cell sequencing in a sponge microbiome.

PNAS Nexus · Mar 2, 2022Peer-reviewedMicrobiology

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

K. Inoue, M. Karasuyama, R. Nakamura, M. Konno, D. Yamada, K. Mannen, T. Nagata, Y. Inatsu, K. Yura, O. Beja, H. Kandori, I. Takeuchi

The paper uses a machine learning model to identify red-shifted rhodopsins, finding 80% of selected candidates show red-shift gains, with some having desirable ion-transporting properties.

bioRxiv · Apr 21, 2020Biochemistry