The 4th inter-disciplinary area Seminar will be held on Thursday, September 17.
・Date: Thursday, September 17, 2015
・Time: 6:00 pm ‐ 8:00 pm
・Venue : Tohoku University Graduate School of Medicine
・Title: Genomic medicine based on Information science
・Lecturer : Prof. Masao Nagasaki
The 62nd In Silico Megabank Research Seminar will be held on Friday, August 21. This Time, we will be welcoming Dr. Ashwini Patil, Institute of Medical Science, The University of Tokyo as our lecturer, and she will be speaking on identifying active gene sub-networks from time-course gene expression profiles using a network analysis method.
・Date: Friday, August 21, 2015
・Time: 5:00 pm ‐ 6:30 pm
・Venue : Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Identifying active gene sub-networks from time-course gene expression profiles using a network analysis method
・Lecturer : Ashwini Patil (The Institute of Medical Science, The University of Tokyo )
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract : Time-course gene expression profiles are frequently used to study cellular response to stimulus and to infer molecular pathways involved in cellular response. I will introduce a method to identify active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. The method uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. We used this method to identify response pathways in the innate immune response using time course gene expression profiles of activated immune cells1 as well as the yeast osmotic stress response2. Using this method, we are now comparing the regulatory networks responsible for the distinct immune outcomes produced from different pathogens. Using these response networks, we would like to identify unique regulatory genes associated with each pathogenic component to help better understand the ways in which the immune response differs for distinct pathogens.
・Organizer : Masao Nagasaki
A research group at Tohoku Medical Megabank Organization (ToMMo) has successfully developed the Japonica ArrayTM which is the first ever SNP array optimized for Japanese population.
The aim of development of Japonica ArrayTM is not only to facilitate the prospective genomic cohort study conducted by ToMMo but also to make a contribution to the genomic medicine studies in Japan.
The array contains 659,253 SNPs, including tag SNPs for imputation, SNPs of Y chromosome and mitochondria, and SNPs related to previously reported genome-wide association studies and pharmacogenomics. The Japonica ArrayTM provides better imputation performance for Japanese individuals than the existing commercially available SNP arrays with both the 1KJPN* panel and 1KGP panel (the International 1,000 genomes project).
*1KJPN：ToMMo constructed the reference panel, which contains 2,100 million single nucleotide polymorphisms (SNPs), from whole-genome sequence data of 1,070 Japanese individuals.
Article
Yosuke Kawai, Takahiro Mimori, Kaname Kojima, Naoki Nariai, Inaho Danjoh, Rumiko Saito, Jun Yasuda, Masayuki Yamamoto, Masao Nagasaki
“Japonica array: Improved genotype imputation by designing a population-specific SNP array with 1,070 Japanese individuals”
Journal of Human Genetics advance online publication, 25 June 2015; doi:10.1038/jhg.2015.68
The genotyping service of the Japonica ArrayTM is now provided by Toshiba Healthcare Company under the license from Tohoku University.
You can download the SNP list designed on Japonica ArrayTM from this website.
http://nagasakilab.csml.org/en/japonica
The 61st In Silico Megabank Research Seminar will be held on Thursday, April 16 . This Time, we will be welcoming Dr. Ichizo Kobayashi, Graduate School of Frontier Sciences, The University of Tokyo as our lecturer, and he will be speaking on “Epigenetics-driven Evolution: Demonstration through OMICS Comparison of Multiple Sequences within Bacterial Species.”
・Date: Thursday, April 16, 2015
・Time: 10:00 am‐11:00 am
・Venue : Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Epigenetics-driven Evolution: Demonstration through OMICS Comparison of Multiple Sequences within Bacterial Species
・Lecturer : Ichizo Kobayashi (Graduate School of Frontier Sciences, The University of Tokyo)
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract : Concerning the adaptive evolution, a theory on the unit of evolution based on “epigenetic state” was developed in contrast to the idea of “selection from various genomic sequencing.” The epigenetic state was demonstrated in the study with bacteria that can directly pass on to the next generation. Many of DNA methylase of bacteria create restriction-modification system and restriction enzyme recognizes the same sequence. They are “selfish” epigenetics that destroy non-self epigenomes. Excluding cells from which restriction-modification system is excluded through chromosomal breaks forces host bacteria into the certain methylome. In our study, it was demonstrated that the restriction-modification system modulates the recognition sequence, alter the creation of methylome in various ways, and leads changes in the patterns of gene expressions and various phenotypes. Restriction of these restriction-modification systems from internal and external environment, a type of restriction enzyme that cuts bases out as is the case in demethylating enzyme of eukaryote, and degradation and alteration of rRNA genes used for metagenome analysis are also introduced; we would like to discuss the perception of microevolution of microbiome
・Organizer : Yoko Kuroki, Masao Nagasaki
The 59th In Silico Megabank Research Seminar will be held on Friday, March 13. This Time, we will be welcoming Dr. Shuhei Mano, The Institute of Statistical Mathematics as our lecturer, and he will be speaking on “Approximate Bayesian Computation and its Relevant Fields”.
・Date: Friday, March 13, 2015
・Time: 5:00 pm‐6:30 pm
・Venue: Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Approximate Bayesian Computation and its Relevant Fields
・Lecturer: Shuhei Mano（The Institute of Statistical Mathematics）
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: Even though it involves numerical aspects only, Baysian data analysis is difficult to be performed with precision as it is based on complex models. Especially at the time of handling large-scale data, researchers must face the limits of computer performance. Like the analysis of outliers, the descriptive approach is one way to analyze data as it has an advantage in the utilization the scale of data; however, there is an issue that all predictions are based on models. Approximate Bayesian Computation is a method of Baysian data analysis which allows the maximal precision within the computable domain with simulation although it does not provide clear likelihood of the models. Despite the fact that it is based on models, the structure of summary statistics is made algorithmically. Thus, the integration of data modeling and machine learning gives spice to the methodology. In this seminar, the background, the theoretical base and method, and possible application to actual problems are introduced.
・Organizer: Yosuke Kawai, Masao Nagasaki
The 58th In Silico Megabank Research Seminar will be held on Friday, March 6. This Time, we will be welcoming Dr. Shingo Tsuji, Research Center for Advanced Science and Technology, The University of Tokyo as our lecturer, and he will be speaking on “Facts and Future of the Data Analysis in the Field of Life Science”.
・Date: Friday, March 6, 2015
・Time: 4:00 pm ‐ 5:30 pm
・Venue: Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Facts and Future of the Data Analysis in the Field of Life Science
・Lecturer: Shingo Tsuji （Research Center for Advanced Science and Technology, The University of Tokyo）
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: In this seminar, my own research results and experience are introduced and I would like to discuss with you on the directionality of that Computational biology should take. The seminar will cover a broad range of topics, and the followings are some of them. As the example of forecast using machine learning algorithms, effect prediction of anticancer agents by Random forests method is introduced. As a case of multi-omics data analysis of cancer, such analyses of 11 types of cancer using the analysis of gene expression data and DNA methylation data for colon cancer and Deep Learning are introduced. Network is often used as a channel of expressing biological knowledge, and I would like to discuss how to extract knowledge from such network. Finally, I would like to consider the social responsibility of Computational biology as presently genomic information is often applied into medicine and health care industry.
・Organizer: Naoki Nariai, Masao Nagasaki
The 57th In Silico Megabank Research Seminar will be held on Friday, February 27. This Time, we will be welcoming Dr. Mahito Sugiyama, The Institute of Scientific and Industrial Research, Osaka University as our lecturer, and he will be speaking on “Finding Statistically Significant Structure from Big Data ”.
・Date: Friday, February 27, 2015
・Time: 5:00 pm‐6:30 pm
・Venue: Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Finding Statistically Significant Structure from Big Data
・Lecturer: Mahito （The Institute of Scientific and Industrial Research, Osaka University）
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: Data mining, whose purpose is knowledge discovery from the big data, is utilized in various fields from basic science such as chemistry and biology to the application to management and marketing. Especially, the development of approaches to find combinational structure hiding in data such as gene pairs co-occurred and expressed and the structure shared by compounds with specific activation is the center of the topic concerning data mining. However, although securing the statistical significance on the discovered knowledge, in other words, the calculation of P value is a requirement in major application domain for data mining including biological science, there was no adequate focus for a long time. In this lecture, initiatives for this rapidly advancing research topic in recent year, triggered by the literature of Terada et al. presented at PNAS in 2013 are introduced. Furthermore, a special attention is paid to the recent outcomes that enable resolution to this matter by combining superfast algorithm developed in the field of data mining and multiple testing procedures developed in the field of statistics.
・Organizer: Takahiro Mimori, Masao Nagasaki
The 56th In Silico Megabank Research Seminar will be held on Friday, February 13.
This Time, we will be welcoming Dr. Kosuke Teshima, Faculty of Sciences, Kyushu University as our lecturer, and he will be speaking on “Population Genetics and Coalescent Theory ”.
・Date/Time: February 13(Friday) 17:00‐18:30
・Venue: Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Population Genetics and Coalescent Theory
・Lecturer: Kosuke Teshima （Faculty of Sciences, Kyushu University）
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: Various researchers especially having an interest in evolution are giving considerable attention to estimating the evolutionary change of organisms from genomic diversity data. Here, the definition of the evolutionary change is a change of organisms in a very long evolutionary timescale. Population genetics is thought to be effective as a theoretical analysis framework when estimating the evolutional change based on genomic diversity data. In this seminar, the perception of evolutionary process and basic analysis method in population genetics are first introduced. Then, the overview of coalescent theory, a frequently used theory in recent population genetic analysis, is provided. Coalescent theory is mainly used in the estimation of evolution parameters in association of the recent development of Bayesian method using calculators. In addition, as examples of studies using coalescent theory, two cases are introduced: a study examining the coincidence probabilities of Y-chromosome haplotype in forensic medicine and another study examining the evolutionary process of overlapping gene using coalescent theory.
・Organizer: Yosuke Kawai, Masao Nagasaki
The 55th In Silico Megabank Research Seminar will be held on Friday, February 6. This Time, we will be welcoming Dr. Yukinori Okada, Tokyo Medical and Dental University as our lecturer, and he will be speaking on “Elucidation of Disease Pathology, New Drug design, and Challenges to Epidemiology with Genetic Statistical Analysis ”.
・Date/Time : February 6 (Friday) 17:00‐18:30
・Venue : Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title : Elucidation of Disease Pathology, New Drug design, and Challenges to Epidemiology with Genetic Statistical Analysis
・Lecturer : Yukinori Okada (Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University)
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: Genetic Statistics is a research area that reveals the connection between the genetic information and trait information in living organisms through statistical analysis. It has been more than 10 years since the human genome sequences was mapped, and now we are in the era when genomic information of several thousands and hundreds of thousands of samples is available. Through massive human genome analysis based on such a large quantities of genomic information, various genes associated with the onset of human diseases have been identified. In addition, genomic statistical analysis which reconciles acquired disease-related genes with various biological and medical databases and drug target gene groups in a cross-sectional manner have slowly revealed its contribution to the additional elucidation of disease pathology and new drug design though drug repositioning. Various approaches from the aspect of genetic statistical analysis has been implemented in order to resolve issues pointed out by epidemiologic studies such as changes in disease complications based on the combination of certain diseases. In this seminar, the results of genetic statistical analysis which we have conducted targeting at various human diseases are introduced and future prospect of human genomic analysis is discussed.
・Organizer : Yumi Yamguchi, Masao Nagasaki
The 54th In Silico Megabank Research Seminar will be held on Friday, January 30. This Time, we will be welcoming Dr. Masaki Nishioka, Graduate School of Medicine, The University of Tokyo as our lecturer, and he will be speaking on “Searching Genome Architecture that Brings Variability to Brain Functions: Focusing on LINE 1”.
・Date/Time: January 30 (Friday) 16:00‐17:30
・Venue: Small Conference Room 2(3rd Floor), Tohoku Medical Megabank Building
・Title: Searching Genome Architecture that Brings Variability to Brain Functions: Focusing on LINE 1
・Lecturer: Masaki Nishioka (Department of Molecular Psychiatry, Graduate School of Medicine, The University of Tokyo)
＊This lecture is transferable as a class in the medical research-related lecture course.
・Abstract: Genetic studies such as on psychiatric disorders have been actively conducted in order to understand various brain functions from the aspect of genes. The relationship between specific gene polymorphisms/ genetic variants and psychic functions has been reported in many studies; however, most of them have low odds ratio and penetration rate. Thus, it is thought that various genes are complexly associated. We hypothesized that, in addition to gene polymorphisms and genetic variants at individual level, somatic mutations occurred in the process of brain development were accountable of the variability in brain functions such as the development of psychiatric disorders, and we have proceeded with the analysis of somatic mutations in human brain. Special attention is paid to retrotransposon LINE-1.
A s the study methods, we utilize 1) an in silico method in which we extract the LINE-1 array from full genome sequence analysis data and identify novel insertion mutation and organ- (cell type-) specific somatic mutations and 2 ) a method (L1Hs-seq) that determines the location of insertion with the next generation sequencer and TAIL-PCR which uses specific primers for L1Hs that has an autonomic activity. In this lecture, we report the results of the ongoing preliminary examinations and discuss the future of the analysis of somatic mutations in brain and relationship with psychiatric disorders based on the interpretation of the data.
・Organizer: Yukuto Sato, Masao Nagasaki