Vandenbon Alexis

Affiliation and department：

Research field (1)： Systems genomics

Papers (43)：

Takuya Uehata, Shinnosuke Yamada, Daisuke Ori, Alexis Vandenbon, Amir Giladi, Adam Jelinski, Yasuhiro Murakawa, Hitomi Watanabe, Kazuhiro Takeuchi, Kazunori Toratani, et al. Regulation of lymphoid-myeloid lineage bias through Regnase-1/3-mediated control of Nfkbiz. Blood. 2023
Alexis Vandenbon, Diego Diez. A universal tool for predicting differentially active features in single-cell and spatial genomics data. Scientific reports. 2023. 13. 1. 11830-11830
Alexis Vandenbon, Rin Mizuno, Riyo Konishi, Masaya Onishi, Kyoko Masuda, Yuka Kobayashi, Hiroshi Kawamoto, Ayako Suzuki, Chenfeng He, Yuki Nakamura, et al. Murine breast cancers disorganize the liver transcriptome in a zonated manner. Communications biology. 2023. 6. 1. 97-97
Masanori Yoshinaga, Kyuho Han, David W Morgens, Takuro Horii, Ryosuke Kobayashi, Tatsuaki Tsuruyama, Fabian Hia, Shota Yasukura, Asako Kajiya, Ting Cai, et al. The N6-methyladenosine methyltransferase METTL16 enables erythropoiesis through safeguarding genome integrity. Nature communications. 2022. 13. 1. 6435-6435
Ai Yaku, Tadakatsu Inagaki, Ryotaro Asano, Makoto Okazawa, Hiroyoshi Mori, Ayuko Sato, Fabian Hia, Takeshi Masaki, Yusuke Manabe, Tomohiko Ishibashi, et al. Regnase-1 Prevents Pulmonary Arterial Hypertension Through mRNA Degradation of Interleukin-6 and Platelet-Derived Growth Factor in Alveolar Macrophages. Circulation. 2022. 101161CIRCULATIONAHA122059435

ｍore...

MISC (1)：

Michio Tomura, Ryoyo Ikebuchi, Shunsuke Teraguchi, Alexis Vandenbon, Shand H. W. Francis, Tetsuya Honda. A rare subset of skin-tropic regulatory T cells expressing Il10/Gzmb inhibits the cutaneous immune response. JOURNAL OF IMMUNOLOGY. 2017. 198. 1

Books (1)：

Lectures and oral presentations (17)：

Exploratory analysis of various single-cell and spatial transcriptomics data types
(Institute for Quantitative Biosciences student seminar, the University of Tokyo 2022)
singleCellHaystack: A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
(Tsukuba Bioinformatics Assembly 2021)
singleCellHaystack: A clustering-independent method for predicting differentially expressed genes in single cell transcriptome data
(1st International Symposium of CCII -Bioinformatics and its application to cancer and other diseases 2021)
singleCellHaystack: A clustering-independent method for predicting differentially expressed genes in single cell transcriptome data
(Human Cell Atlas Asia Meeting 2020 2020)
Prediction of regulatory mechanisms of gene expression at the epigenetic, transcriptional and post-transcriptional level
(IIBMP2019 2019)

ｍore...

Work history (2)：

※ Researcher’s information displayed in J-GLOBAL is based on the information registered in researchmap. For details, see here.

Return to Previous Page