Day 2 :
University of Tokyo, Japan
Time : 09:30-10:15
Satoru Miyano, PhD, is the Director of Human Genome Center, Institute of Medical Science, University of Tokyo. He received the B.S. (1977), M.S. (1979) and PhD (1984), all in Mathematics from Kyushu University, Japan. He has been working in the field of Bioinformatics and joined Human Genome Center as a professor in 1996. His research mission is to develop “Computational Medical Systems Biology towards Genomic Personalized Medicine”, in particular, cancer research and clinical sequence informatics. He has been involved as PI with MEXT Scientific Research on Innovative Areas “Systems Cancer Project”, “Systems Cancer in Neodimension”, the International Cancer Genome Consortium, MEXT Large-Scale Data Analysis with K computer, and Post-K Computer Project. He is an ISCB Fellow 2013.
We present our computational methods and their analyses in Cancer Systems Biology that use the supercomputers at Human Genome Center of The University of Tokyo and K computer at RIKEN Advanced Institute of Computational Science. The first challenge is a pipeline Genomon (https://github.com/Genomon-Project/) (Fig. 1) for cancer genome analysis that is a suite of bioinformatics tools for analyzing cancer genome data (WGS, WES, RNA-seq). It enables us to perform very sensitive and accurate detection of most types of genomic variants (single nucleotide variants, short indels, mid-size indels and large scale structural variations), and transcriptomic changes (gene fusions, aberrant splicing patterns). It adopts an efficient job scheduling framework that enables us easily analyzing several hundreds of genome and transcriptome sequencing data simultaneously. We present some of our recent contributions to cancer genomics with Genomon [1-2].
The second is computational strategy for unraveling gene networks and their diversity lying over genetic variations, mutations, environments and diseases from gene expression profiles of cancer cells. We developed methods for exhibiting how gene networks vary from patient to patient according to a modulator, which is any score representing characteristics of cells, e.g. survival, drug resistance [3-4]. We also developed a microRNA/mRNA gene network analysis with Bayesian network method that revealed subnetworks with hub genes that may switch cancer survival . On-going cancer research is also introduced, including a discovery of the first lncRNA modulating MYC gene regulation using K computer.