Development of a robust computational platform for data-driven epigenome analysis

We aim to develop a computational platform for comparative epigenomic analysis of datasets obtained from multiple NGS assays (Figure). This platform will integrate ChIP-seq, RNA-seq, Hi-C and other types of genomic data (e.g., GWAS annotation) and implement semi-automated genome annotation to characterize the entire genomic region in more detail. We will also develop a data imputation strategy that predicts the enrichment pattern of ChIP-seq and other epigenomic data by leveraging information from other relevant cell lines and epigenomic marks available in existing databases. This approach can reduce technical noise in raw NGS data and generate virtual data of missing samples in a large dataset in silico. This platform can significantly reduce the cost of both NGS data generation and computational analysis for large-scale epigenomic analysis, especially for valuable biological samples (e.g., clinical samples of rare diseases).
We have developed a new method, HiC1Dmetrics, which can efficiently extract a variety of one-dimensional features from Hi-C data. This method facilitates epigenomic analysis that takes into account three-dimensional structure, which has been difficult with a traditional approach.

Functional analysis of cohesin complex

From the biological perspective, we are interested in the gene expression and three-dimensional chromatin folding regulated by the cohesin complex. Cohesin is involved in gene regulation by various mechanisms, including the mediation or insulation of enhancer-promoter loops, the formation of topologically associating domains (TADs) by loop extrusion, and RNA polymerase II elongation, cooperating with various factors such as cohesin loader, cohesin acetyltransferase, insulator-binding factor CTCF, and super-elongation complex (Figure). Although the mutation of cohesin and its related factor causes a developmental syndrome with a complex phenotype Cornelia de Lange syndrome (CdLS), and several cancers, an underlying molecular mechanism is still unclear. We are performing large-scale multi-omics analysis using Hi-C, RNA-seq, and ChIP-seq on human and mouse cells to comprehensively elucidate the details of the diverse functions of cohesin and their relationship to diseases caused by mutations in cohesin.

Various analysis using single-cell methods

Single-cell analysis, which observes genomic information at the single-cell level, is used to observe cellular heterogeneity in tissues　and cell differentiation trajectories. We have developed a computational platform for single-cell analysis, ShortCake. It can dramatically reduce the cost of the tool installation process, which is an initial barrier for many researchers. We are also promoting single-cell analysis through single-cell analysis workshops using ShortCake.
We are also interested in a gene network analysis using single-cell data, and developed a new approach EEISP, which robustly estimates gene co-expression and and mutual exclusivity from sparse scRNA-seq data. The network-level comparison allows us to identify novel candidate marker genes that could not be obtained by conventional gene expression variation analysis.