Project

In the field of biology, due to the advancement of DNA sequencing and other high-throughput technologies, a vast amount of information has been accumulated, from microscopic molecular and cellular information to macroscopic individual characteristics. In order to exploit the wealth of information from the huge data space, it is essential to appropriately handle and analyze data with the knowledge of not only biology (of the targets) but also information science and statistics. In our laboratory, we aim to uncover the unknown relationships (missing links) between genes and biological functions through meta-integrative analysis techniques that comprehensively compare data obtained by different labs in a variety of conditions for different purposes.

Decoding the Role of Epigenetic Fluctuation in Stochastic Cell State Transitions (ACT-X project)

The main goal of my project is to understand why even cells under the same conditions show the heterogeneity in their epigenetic states, which leads to random changes during the cell state transitions. By using advanced deep learning techniques, I aim to overcome biases in single-cell multiome analysis and establish a flexible model that explains how environmental and stochastic fluctuations bring about diverse epigenetic changes, resulting in random shifts in cell behavior, especially during the stochastic cell state transition in the context of reprogramming and differentiation.

Single-cell epigenome analysis

• Kawaguchi RK, Tang Z, Fischer S, Rajesh C, Tripathy R, Koo PK, Gillis J. “Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.”, Briefings in Bioinformatics, bbac541, 2022.
• Sheu YJ, Kawaguchi RK, Gillis J, Stillman B. “Prevalent and dynamic binding of the cell cycle checkpoint kinase Rad53 to gene promoters.”, eLife, 11:e84320, 2022.

RNA structure analysis

• Kawaguchi R, Kiryu H, Iwakiri J and Sese J. “reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction”, BMC Bioinformatics 20 (Suppl 3):130, 2019.
• Kawaguchi RK and Kiryu H. “Genome-Wide RNA Secondary Structure Prediction”, RNA Structure Prediction, Methods in Molecular Biology 2586. (Protocol review)

Machine learning for small data in medical research

• Kawaguchi RK†, Takahashi M*,†, et al. “Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals”, Cancers (Basel), 13(14), 3611, 2021.
• Takahashi S, et al. “Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities”, Cancers (Basel), 13(6):1415, 2021.