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Single-cell sequencing techniques from individual to multiomics analyses

Experimental & Molecular Medicine 2020년 52권 9호 p.18 ~ 18
Kashima Yukie, Sakamoto Yoshitaka, Kaneko Keiya, Seki Masahide, Suzuki Yutaka, Suzuki Ayako,
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 ( Kashima Yukie ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences
 ( Sakamoto Yoshitaka ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences
 ( Kaneko Keiya ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences
 ( Seki Masahide ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences
 ( Suzuki Yutaka ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences
 ( Suzuki Ayako ) - University of Tokyo Graduate School of Frontier Sciences Department of Computational Biology and Medical Sciences

Abstract


Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.

키워드

Next-generation sequencing; RNA sequencing

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