Because of rapid improvements in sequencing technologies, many types 13of transcriptomic, genomic and epigenomic data have been generated and made publicly available. Such data resources are potentially useful for the elucidation of biological systems and the development of medical tools by per-forming large-scale integrative analyses. Our mission is promoting such data-driven studies in the biomedical field and developing component technologies to efficiently reuse large-scale biomedical data by employing data engineering technologies.For this purpose, we are working on several research projects. One of them is the development of a QC pipeline and methods for reusing and evaluat-ing public single-cell RNA-seq data and the construction of a public database named “SCPortalen” (http://single-cell.clst.riken.jp/). Our new QC method (SkewC) can identify “skewed cells,” that can affect further downstream analy-ses with single-cell RNA-seq data (See figure). With this method, we found that such cells are present in most publicly available single-cell RNA-seq data. Another project is the development of a reference transcription start sites (refTSS: http://reftss.clst.riken.jp/), which can be used as a reference point to integrate many types of transcriptome and epigenome data to enable the study of transcriptional regulation. Our team is also working on data coordination in the FANTOM6 project, in which we aim to identify functions of lncRNAs (http://fantom.gsc.riken.jp/6/). We are also working on studies targeting several diseases: a transcriptome analysis of human blood samples from aged patients with frailty phenotype; and a transcriptome analysis to develop a diagnostic tool for mycetoma, an infectious disease on the WHO listing of neglected tropi-cal diseases.Along with these research projects, we are working to provide and support the information infrastructure for several IMS laboratories. We are also investi-gating infrastructure and regulatory requirements for handling human-derived sequence data in on-premises and cloud environments in a collaboration with other IMS laboratories.Figure: SkewC method to identify skewed cells in single-cell RNA-seq dataThe method can identify skewed cells, which affect downstream analyses, including clustering and differen-tially expressed genes in single-cell RNA-seq datasets.Recent Major PublicationsAbugessaisa I, Ramilowski JA, Lizio M, Severin J, Hasegawa A, Harshbarger J, Kondo A, Noguchi S, Yip CW, Ooi JLC, Tagami M, Hori F, Agrawal S, Hon CC, Cardon M, Ikeda S, Ono H, Bono H, Kato M, Hashimoto K, Bonetti A, Kato M, Kobayashi N, Shin J, de Hoon M, Hayashizaki Y, Carninci P, Kawaji H, Kasukawa T. FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs. Nucleic Acids Res 49, D892-D898 (2020)Ramilowski JA, Yip CW, Agrawal S, Chang JC, Ciani Y, Kulakovskiy IV, Mendez M, Ooi JLC, Ouyang JF, Parkinson N, Petri A, Roos L, Severin J, Yasuzawa K, Abugessaisa I, Akalin A, Antonov IV, Arner E, Bonetti A, Bono H, Borsari B, Brombacher F, Cameron CJ,… Cannistraci CV, Carde-nas R, Cardon M, Chang H, Dostie J, Ducoli L, Favorov A, Fort A, Forrest ARR, Guigó R, Hoffman MM, Hon CC, Kasukawa T, Kauppinen S, Kere J, Lenhard B, Schneider C, Suzuki H, Yagi K, de Hoon MJL, Shin JW, Carninci P. Functional annotation of human long noncoding RNAs via molecular phenotyping. Genome Res. 30,1060-1072 (2020)Bonetti A, Agostini F, Suzuki AM, Hashimoto K, Pascarel-la G, Gimenez J, Roos L, Nash AJ, Ghilotti M, Cameron CJF, Valentine M, Medvedeva YA, Noguchi S, Agirre E, Kashi K, Samudyata, Luginbühl J, Cazzoli R, Agrawal S, Luscombe NM, Blanchette M, Kasukawa T, de Hoon M, Arner E, Lenhard B, Plessy C, Castelo-Branco G, Orlando V, Carninci P. RADICL-seq Identifies General and Cell Type-Specific Principles of Genome-Wide RNA-chromatin Interactions, Nat. Commun 11, 1018 (2020)Invited presentationsKasukawa T. “Data coordination for large-scale tran-scriptome data production in FANTOM and single-cell projects” RIKEN Hackathon FY2019 Open Symposium (Kobe, Japan) February 2020Laboratory for Large-Scale Biomedical Data TechnologyTeam Leader: Takeya Kasukawa
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