eC2ENS00.105.000.000.105.000.000.105.000.0egarevoceneGsexpezs lli TypicalSkewedBecause of rapid improvements in sequencing technologies, many types 15Typical cellsnFeature_RNA10,000506e+057,5004e+055,0002,5002e+050e+002010−10−20LiveDead−10−51001Gene body percentile D1001Gene body percentile 5040202K1KLiveDead10AB CE0t0li ni0F05Skewed cellsnCount_RNA(5'−> 3')percent.mt60TypicalSkewed(5'−> 3')tSNE1SkewedSkewed&deadTypicalTypical&deadDead:TypicalDead:Skewed(5'−> 3')1001Gene body percentile 50infrastructure for several IMS laboratories.of 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 have several ongoing research projects. One of them is the development of a QC pipeline and methods for reusing and evaluating pub-lic single-cell RNA-seq data and the construction of a public database named “SCPortalen” (https://single-cell.riken.jp/). Our new QC method (SkewC) can identify “skewed cells,” which can negatively 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 set of transcription start sites (refTSS: https://reftss.riken.jp/), which can be used for the quantification of 5’-end RNA-seq data, and also can be used as a platform for integrating many types of transcriptome and epigenome data to promote the study of transcrip-tional regulation. We are also working on studies targeting human pathology/disease: the transcriptome analysis of human blood samples from aged patients with frailty phenotype and the 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 provide and support the information Figure: An example of a SkewC single-cell RNA-seq quality assessmentTypical/skewed cells: QC annotation with SkewC; Dead/live cells: QC annotation by the original data producers. (A-B) Gene body coverage plots for typical and skewed cells. (C) The gene body coverage plot for dead cells (colored by typical and skewed cells with SkewC). (D) Violin plots showing Seurat QC metrics. The violin plot split by the SkewC annotations and dead cells, shown as black and blue circles, respectively. (E) The t-SNE clus-tering plots with typical and skewed cell annotations. Dead and live cells are highlighted by symbols. (F) The distribution of cell sizes between typical and skewed cells, dead and live are highlighted by symbols.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 (2021)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, Cardenas R, Cardon M, Chang H, Dostie J, Ducoli L, Favorov A, Fort A, Garrido D, Gil N, Gimenez J, Guler R, Handoko L, Harsh-barger J, Hasegawa A, Hasegawa Y, Hashimoto K,…, 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 an-notation 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. “FANTOM project and data resource for understanding the transcription in the mammalian genomes” BioC Asia (Online) November 2021Laboratory for Large-Scale Biomedical Data TechnologyTeam Leader: Takeya Kasukawa
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