It still remains mostly unknown which of the variations or mutations occurring in the human genomes contribute to etiology of diseases. We employ versatile applications of next generation sequencing technologies, such as Whole Genome/Exome Seq, RNA Seq, ChIP Seq and Bisulfite Seq to understand the biological meaning of the identified genomic mutations. Advent of the next generation sequencing technologies has enabled us to analyze thousands of human genomes. Consequently, a rapidly increasing number of mutations have been identified and associated with various diseases, such as cancers. However, it still remains elusive how these mutations invoke changes in epigenome, transcriptome, or proteome functions. For the diseases as exemplified below, we are conducting an integrative analysis of multi-omics data, namely DNA methylation, histone modifications, biding patterns of transcriptional regulatory factors and gene expression patterns. Furthermore, to complement currently undetectable layers of transcriptome regulations, we are developing novel methods, based on the latest genomic technologies, such as next generation sequencing, single cell analysis and single molecule sequencing technologies. Also, as a one of the representative sequencing centers in Japan, we are distributing the next sequencing platforms and the related technologies widely to the research community.


1. Cancer Genomics

As collaboration with several hospitals and laboratories of clinical sequencing, we have analyzed the mutation patterns of various types of cancers, including lung, colon and stomach cancers. We have found that the mutated genes are mostly distinct depending on patients and cancer types, with rare exceptions of the TP53, KRAS and EGFR genes. With rare mutual overlaps, it is difficult to statistically discriminate so-called driver mutations, which serve as a direct driving force to carcinogenesis, from so-called passenger mutations, which occur in the human genomes as a consequence of chromosomal instability in cancers, thus, have no functional relevance. Moreover, in spite of supposed importance, almost no clue has been obtained for the mutations which invoke abnormal transcriptional regulations. To address these issues, we have established an experimental system to collect genome, epigenome and transcriptome data from the same cellular material and have started theee data production. By integrating such multi-omics data, we are investigating epigenomic and transcriptomic consequences of the genomic mutations.

2. Technology Development and Modelling

Recent genome-wide analyses have revealed that gene expression regulations, such as the regulations at transcriptional elongation, RNA logistic and RNA degradation, play no less important roles than transcriptional initiations. We are trying to develop a new method to evaluate the contribution from these factors, using the latest genome-related technologies. We have constructed an experimental system in which correlation between DNA mutations at every base position can be associated with promoter activities for thousands of genes simultaneously. Generated data is further processed to construct a model, using machine-learning and statistical inference technologies, to predict eventual transcript levels. We are also including the data obtained from the emerging technologies measuring post-transcriptional regulatory factors to the model. Eventually, we believe such a model should be essential to understand biological meaning of the genomic variations of regulatory roles in the humans.

3. In Field Analysis of Infectious Diseases

Frequently, behaviors of human immune systems in response to pathogens are significantly different in the field from those in laboratory conditions. We have a field base in Indonesia and are analyzing the mutual correlation between the host-pathogens at every omics layer, particularly focusing on malaria parasites.

References: Yamashita et al, Genome Res, 2011 Suzuki et al, PLoS ONE, 2013 Irie et al, NAR, 2011



Yutaka Suzuki, Ph.D
1994 Department of Chemistry, University of Tokyo B.Sc.
1996 Department of Interdisciplinary Sciences, University of Tokyo M.Sc.
1999 Department of Interdisciplinary Sciences, University of Tokyo Ph.D.
1999-2000 Research Associate, Genome Science Center, RIKEN, Japan
2000-2003 Research Associate, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Japan
2003- present Associate Professor, Department of Medical Genome Science, the University of Tokyo, Japan
2013 Professor, Department of Computatinal Biology, the University of Tokyo, Japan