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Nakato Lab (IQB, Large-Scale Bioinformatics Research Division)
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The lab uses computation to uncover how gene regulation works from large-scale genome data.
AboutAbout the lab
Computational genomics and large-scale omics analysis
Nakato Lab conducts research that uses computation to analyze genomes, epigenomes, gene transcription, 3D genome structure, and single-cell data.
The lab aims to clarify how the same genome gives rise to different gene activity depending on cell type, cell state, and disease.
Rather than only using existing analysis methods, the lab develops the models and methods that its questions require.
Members with different backgrounds in life science and informatics work together on shared questions.
Key terms
- Epigenome
- The full set of marks and information that determine which genes are active without changing the DNA sequence
- Deep learning
- A machine learning method that learns features from large amounts of data using neural networks with many stacked layers
- Cohesin
- A protein complex that holds DNA together in ring-like loops. It is involved in forming the 3D structure of the genome
NewsLab news
A doctoral student received the Young Innovator Award at CSH Asia "AI and Biology"
A paper by Associate Professor Nakato and colleagues was published in Bioinformatics (Oxford, England)
A paper by Associate Professor Nakato and colleagues was published in Heliyon
A paper by Associate Professor Nakato and colleagues was published in Genes to cells : devoted to molecular & cellular mechanisms
A paper by Associate Professor Nakato and colleagues was published in DNA research : an international journal for rapid publicati…
ResearchResearch themes
By integrating data on DNA sequences, epigenomes, regulatory factors, functional RNAs, and 3D genome structure, the lab clarifies how different gene regulation arises depending on cell type, cell state, and disease. Using deep learning, network theory, and statistical modeling, it reconstructs the structures in which multiple elements act cooperatively and antagonistically to make the genome function.
*Illustrative imageReading regulatory mechanisms from sequence
Using genomic deep learning models and epigenome representation learning, the lab investigates which sequence elements combine to function in which cellular contexts. Through in silico mutation analysis and evaluation against ground-truth data, it identifies the regulatory rules the models capture and the conditions they fail to capture.
*Illustrative imageFrom multilayer data to regulatory networks
The lab integrates data such as ChIP-seq, RNA-seq, Hi-C, and single-cell data into multilayer networks. By using not only positive relationships but also mutually exclusive and antagonistic ones, it extracts interactions that ordinary clustering cannot distinguish.
*Illustrative imageDiscovering and validating functional modules
Focusing on cohesin, HOX factor groups, protein complexes, and more, the lab finds "functional modules," in which multiple factors and genomic regions work cooperatively to carry out a single function. It compares how these modules are rearranged across cell types, diseases, and perturbations, and validates them with independent data and experiments.
MembersFaculty
Nakato Lab members include faculty, technical and academic support staff, doctoral and master's students, research students, interns, and collaborating researchers.As of 2026
- Associate ProfessorNakato RyuichiroPublications ↗
- Project Assistant ProfessorYuya Nagaoka
- Project Assistant ProfessorJina Mirai Oba
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Model of Nucleosome Array” Rkott / CC BY-SA 4.0 (cropped)
- Reading regulatory mechanisms from sequence: “DNA sequence” MIKI Yoshihito from Sapporo City,Hokkaido., JAPAN / CC BY 2.0 (cropped)
- From multilayer data to regulatory networks: “Human interactome” Keiono / CC BY-SA 3.0 (cropped)
- Discovering and validating functional modules: “Chromatin fibers (261 19) Mitosis; Xenopus egg” Doc. RNDr. Josef Reischig, CSc. / CC BY-SA 3.0 (cropped)
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