BDR researchers coming from diverse research fields are working together to achieve higher goals.

Seminars & Symposia

Seminars & Symposia

BDR hosts annual symposium and regular seminars inviting international scientists in life science.

Careers & Study

Careers & Study

BDR embraces people from diverse backgrounds, and strives to create an open and supportive setting for research.



BDR communicates the appeal and significance of our research to society through the use of various media and activities.



From research, events, people and everything in between, find out what’s going on at RIKEN BDR.

About Us

About Us

Exploring the scientific foundations of life through interdisciplinary approaches to address society’s problems.

二階堂 愛チームリーダーの写真

Team Leader
Itoshi Nikaido Ph.D.

Laboratory for Bioinformatics Research

Location: Kobe / Developmental Biology Buildings, Wako / Information Science Bldg.

E-mailitoshi.nikaido [at] riken.jp

Please replace [at] with @.

A multicellular organism is orchestrated by cell growth, death, differentiation, and communication at the single-cell level. To understand various crucial biological phenomena, we should massively perturb and measure transcriptomes and epigenomes at the single-cell level.

Our team will develop novel methods of comprehensive analysis and perturbation of transcriptome and epigenome at the single-cell level, in particular, by applying massively parallel DNA sequencing, genome editing, microfluidics, and machine learning. We focus on the development of methods for quantifying and controlling cell function, fate, and cell-cell communication at the single-cell level.

We have already reported novel single-cell RNA-sequencing methods, such as Quartz-Seq and RamDA-seq,which are highly reproducible and sensitive methods of quantifying single-cell transcriptome. Our team will not only develop new techniques but also collaborate with various life scientists within and outside of RIKEN using our new sequencing technologies.

With these techniques, our team seeks to promote the social well-being by contributing insights into how humans can achieve health and longevity.

Research Theme

  • Development of novel single-cell omics techniques
  • Collaboration with various biologists to apply novel single-cell omics technologies

Selected Publications

Lin CW, Septyaningtrias DE, Chao HW, et al.
A common epigenetic mechanism across different cellular origins underlies systemic immune dysregulation in an idiopathic autism mouse model.
Molecular Psychiatry 27(8), 3343-3354 (2022) doi: 10.1038/s41380-022-01566-y

Ochiai H, Hayashi T, Umeda M, et al.
Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells.
Science Advances 6(25), eaaz6699 (2020) doi: 10.1126/sciadv.aaz6699

Ozaki H, Hayashi T, Umeda M, Nikaido I.
Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets.
BMC Genomics 21, 177 (2020) doi: 10.1186/s12864-020-6542-z

Tsuyuzaki K, Sato H, Sato K, Nikaido I.
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.
Genome Biology 21, 9 (2020) doi: 10.1186/s13059-019-1900-3

Mereu E, Lafzi A, Moutinho C, et al.
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.
Nature biotechnology 38(6), 747-755 (2020) doi: 10.1038/s41587-020-0469-4

Sato K, Tsuyuzaki K, Shimizu K, Nikaido I.
CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing.
Genome Biology 20, 31 (2019) doi: 10.1186/s13059-019-1639-x

Sasagawa Y, Danno H, Takada H et al.
Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.
Genome Biology 19, 29 (2018) doi: 10.1186/s13059-018-1407-3

Hayashi T, Ozaki H, Sasagawa Y, et al.
Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs.
Nature Communications 9, 619 (2018) doi: 10.1038/s41467-018-02866-0

Matsumoto H, Kiryu H, Furusawa C, et al.
SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation.
Bioinformatics 33(15), 2314-2321 (2017) doi: 10.1093/bioinformatics/btx194

Tsuyuzaki K, Morota G, Ishii M, et al.
MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.
BMC Bioinformatics 16, 45 (2015) doi: 10.1186/s12859-015-0453-z

Sasagawa Y, Nikaido I, Hayashi T, et al.
Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.
Genome Biology 14, 3097 (2013) doi: 10.1186/gb-2013-14-4-r31

Adachi K, Nikaido I, Ohta H, et al.
Context-dependent wiring of Sox2 regulatory networks for self-renewal of embryonic and trophoblast stem cells.
Molecular Cell 52, 380-392 (2013) doi: 10.1016/j.molcel.2013.09.002