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Professor Masao Nagasaki and Assistant Professor Naoki Nariai presented on March 18, 2014 at Maidashi Campus of Kyushu University.

http://chromatin.med.kyushu-u.ac.jp/archives/218

The 45th In Silico Megabank Research Seminar will be held on Tuesday, February 18.

This Time, we will be welcoming Dr. Naruya Saito, National Institute of Genetics as our lecturer, and he will be speaking on “Human DNA Evolution in Japanese Archipelago: Past and Present”.

・Date/Time: February 18(Tues.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
・Title: Human DNA Evolution in Japanese Archipelago: Past and Present
・Lecturer: Naruya Saito (National Institute of Genetics )

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: The approach to explain the origin of Japanese people from 2 perspectives such as Jomon-type who have lived since Jomon period and immigrants to ancient Japan after Yayoi period is called the “dual structure model.”  Based on this model, first, the descendant of the Asian group who had long settled in Southeast Asia immigrated to Japanese islands in the Old Stone Age and resulted in the development of Jomon people. Time past and when Yayoi period began, there were immigrants from Northeast Asia. They shared the same ancient population as Jomon Japanese but later went through unique changes, forming separate features such as face from Jomon Japanese. These immigrants from the continent continued to breed with the descendants of the Jomon Japanese who were the indigenous heritage. However, the descendant population of Jomon Japanese in Hokkaido did not breed with such immigrants and developed as Ainu Japanese. Also, Southwest Islands around Okinawa received a number of immigrants from the mainland; however, in comparison to the main Japanese land, characteristics of Jomon Japanese remained strong.  This model was mainly introduced by researchers such as Kazuo Haninara who analyzed human bone data, and the analysis result of genome-wide SNP data also supports the basic outline of this dual structure model. On the other hand, mitochondrial DNA study was the center of focus to directly examine DNA of the ancient human genomes until now. But recently the genome mapping team comprised of my laboratory and National Science Museum succeeded in determining a part of genomic DNA which remained in the human bones excavated from multiple Jomon shell midden sites in Tohoku area. These results clearly show that Jomon Japanese were highly unique across East Eurasian population. In this lecture, evolution of human genomic DNA of those Japanese inhabitants from past and present including the latest results will be introduced.

・Organizer: Yosuke Kawai, Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html

The 47th In Silico Megabank Research Seminar will be held on Friday, February 28.

This Time, we will be welcoming Dr. David duVerle, National Institute of Advanced Industrial Science and Technology as our lecturer, and he will be speaking on “Discovering Combinatorial Gene Interactions in High-Dimensional Data”.

・Date/Time: February 28(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
・Title: Discovering Combinatorial Gene Interactions in High-Dimensional Data
・Lecturer: David duVerle（Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology）

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: In the past decade or so, new technologies in biotech have meant an explosion in the availability of high-dimensional genomic data (microarrays, SNP data, RNA-Seq…): their dimension and noise levels making it necessary to rely on machine learning techniques and statistical models to extract meaningful signal and narrow down the field for further experimental research. In this presentation, I will try to give a very general overview of some of the statistical techniques commonly used to treat high-dimensional data, as well as a more detailed illustration of the technique we developed to identify combinatorial interaction effects in such data.

A crucial aspect of machine-learning when dealing with high-dimensional data, is the concept of sparsity: how much of the input’s variables find their way in the model. By using regularisation techniques (the addition of a tailored penalisation component), it is possible to ensure certain properties of the statistical model (size, elimination of collinear variables…). Another, is the fitting of complex statistical models that cannot be solved analytically, usually requiring optimising a non-linear objective function (e.g to maximise likelihood or minimise empirical error). While relatively simple in application, both techniques require some understanding of the underlying statistical assumption and information theoretic implications, in order to obtain satisfying results.

After giving a brief overview of regularisation techniques and their use in typical regression problems encountered in bioinformatics, I will introduce our recent work, which combines them with data-mining (itemset mining) and fractional programming techniques to fit complex statistical models over (non-linear) combinations of heterogeneous input variables, allowing for example to identify sets of genes (up- or down-regulated) that drive complex phenotypes or clinical observations.

This work was in particular successfully applied to a combination of cDNA microarray and gene mutation copy number data paired with (right-censored) survival data, to identify interactions (and potential synthetic lethals) playing a role in neuroblastoma and breast-cancer.

・Organizer: Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html

The 46th In Silico Megabank Research Seminar will be held on Friday, February 21.

This Time, we will be welcoming Dr. Frith, National Institute of Advanced Industrial Science and Technology as our lecturer, and he will be speaking on “Adapting classic statistical alignment to modern high-throughput DNA”.

・Date/Time: February 21(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
・Title: Adapting classic statistical alignment to modern high-throughput DNA
・Lecturer: Martin Frith（Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology）

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: For many decades, the main way of analyzing biological sequences has been by comparing and aligning them.  This remains true today.  Modern tasks include: comparing whole genomes; aligning bisulfite-converted DNA reads to a genome; aligning long, high-error sequences from single molecule sequencers; aligning ancient or degraded DNA; comparing metagenomic DNA to a protein database. Over the decades, statistically powerful alignment techniques have been developed, including: log likelihood ratio scoring matrices, pair hidden Markov models, and probabilistic gnment.  Unfortunately, these methods are rarely used with modern deep sequencing data, perhaps because they are thought to be too slow.  This talk will demonstrate that they can be made fast enough, and offer great benefits. This talk will also sketch how to incorporate some new features into classic statistical alignment: sequence quality data (phred scores); pairing relationships between DNA reads; and split alignment, where different parts of one query sequence may align to disjoint loci in a genome.  This is useful for genome rearrangements, spliced RNA (including trans-splicing), and even whole genome comparison.  The statistical approach can tell us the reliability (unambiguity) of any part of an alignment.

・Organizer: Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html

The 44th In Silico Megabank Research Seminar will be held on Friday, January 17.

This Time, we will be welcoming Dr. Yoshihito Niimura, Graduate School of Agricultural and Life Sciences, The University of Tokyo as our lecturer, and he will be speaking on “Evolution of Olfactory Receptor Gene of Vertebrates -Genome Changing in Response to the Environment-”.

・Date/Time: January 17(Fri.) 16:00‐17:30
・Venue:  Middle Conference Room (2nd Floor), Building #1, Tohoku University School of Medicine
・Title: Evolution of Olfactory Receptor Gene of Vertebrates -Genome Changing in Response to the Environment –
・Lecturer: Yoshihito Niimura（Graduate School of Agricultural and Life Sciences, The University of Tokyo）

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: There are various odor molecules in our environment, and they are detected by olfactory receptors (OR). OR genes form the largest gene family in mammals. There are approximately 400 in humans, and more than 1,000 in mouse and rats. OR gene family is characterized by huge variance in the number of genes depending on the species and extremely huge numbers of duplications and deletions occur in the process of evolution. In addition, evolution dynamics differ by OR gene; it was found that there exist OR genes producing a variety of progeny and, at the same time, that there are only few genes that are stable in the process of evolution without duplications and deletions. In this seminar, we will focus on the evolution of olfactory receptor gene of vertebrates and take you to the journey, a sense of smell.

・Organizer : Yoko Kuroki, Masao Nagasaki

The 43rd In Silico Megabank Research Seminar will be held on Friday, December 20.

This Time, we will be welcoming Dr. Yoshiaki Tanaka, Yale University School of Medicine as our lecturer, and he will be speaking on “The Rett’s Syndrome transcriptome analysis using patient-derived IPS cells”.

・Date/Time: December 20(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
・Title: The Rett’s Syndrome transcriptome analysis using patient-derived IPS cells
・Lecturer: Yoshiaki Tanaka（Yale School of Medicine, Yale University）

・Abstract: The Rett’s Syndrome (RTT) is one of the girl-specific neurodevelopmental disorders showing symptoms such as stammer or the motor deficit within 6-18 months after birth. More than 90% of Rett’s Syndrome patients have variant in MeCP2 of the Methyl-Binding Domain Proteins. MeCP2 is affected by the X chromosome inactivation since it exists on an X chromosome. Also, MeCP2 is very essential gene for survival; thus, the Rett’s Syndrome patients have variant of MeCP2 only in one of the pairs of X chromosomes. Until now, various knowledges concerning RTT were obtained from MeCP2-deficient mice and human postmortem brain, but disorder mechanism in the early developmental phase of Rett’s Syndrome is not yet fully understood. In late years we have produced iPS cells (RTT—iPS cells) from the fiber blast cells of the Rett’s Syndrome patients and isolated the RTT-wt-iPS cell clone which leads to wild-type MeCP2 expression and the RTT-mu-iPS cell clone which leads to variant MeCP2 expression. Also, at the time of the production of iPS cells, clone that suggests reactivation of X chromosomes was observed; we isolated RTT-bi-iPS cells lead to the expression of both wild-type and variant simultaneously. Transcriptome was analyzed using these RTT-iPS cell clones to examine the effect of MeCP2 on multipotent stem cells in this study. As a result, in addition to the past studies reporting on the involvement of MeCP2 on splicing and cell cycle related genes, we found excess expression of mitochondria-related genes for RTT-mu-iPS cells. Moreover, the excess expression of mitochondria genes was observed even after neurodifferentiation and postmortem brain.  Furthermore, the effect of MeCP2 on X chromosomes was clarified using RTT-bi-iPS cells. These results are important reference for understanding the effect that CeCP2 variants produce in the early development as well as establishing new treatment target.

・Organizer : Riu Yamashita, Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html

The 42nd In Silico Megabank Research Seminar will be held on Friday, December 13.

This Time, we will be welcoming Dr. Masafumi Nozawa, National Institute of Genetics as our lecturer, and he will be speaking on “A novel approach based on interspecies comparison to trace the evolution of dosage compensation.”

・Date/Time: December 13(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
・Title: A novel approach based on interspecies comparison to trace the evolution of dosage compensation
・Lecturer: Masafumi Nozawa（Center for Information Biology, National Institute of Genetics）

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: Evolution of sex chromosomes is normally initiated by assembling of sex-determining genes and sexually antagonistic genes into a pair of autosomes. Recombination between proto-X and proto-Y chromosomes derived from this pair is then suppressed, because these genes must be tightly linked to maintain sexes. This promotes the divergence of these chromosomes and makes the authentic X and Y chromosomes. At the same time, the Y chromosome is gradually degenerated due to the accumulation of mutations and transposable elements. When Y-linked genes are inactivated or deleted due to the inefficacy of natural selection, their orthologous X-linked genes become monoallelic (or hemizygous) in males, which results in dosage imbalance between sexes as well as between chromosomes in males. Since this imbalance could potentially be deleterious in the evolution of heteromorphic sex chromosomes, it has been thought that there must be some mechanisms to compensate the imbalance. In this context, the concept of dosage compensation was proposed. Indeed, this concept has widely been accepted in many organisms, such as humans and nematodes. Yet, most of these conclusions are based on the results that gene expression levels are similar between sexes and/or between chromosomes and it remains to be elusive how dosage compensation has evolved. To trace the evolutionary process of dosage compensation, I have compared the gene expression on the so-called neo-X chromosome in Drosophila pseudoobscura with that on the orthologous autosomes in other species. In this presentation, I would like to discuss about the results obtained so far and introduce my future plans.

・Organizer : Yukuto Sato, Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html

The 41st In Silico Megabank Research Seminar will be held on Tuesday, December 10.

This Time, we will be welcoming Dr. Yu Nishiyama, The Institute of Statistical Mathematics as our lecturer, and he will be speaking on “Topic of Bayesian Inference Using Positive Definite Kernel as the Most Recent Kernel Method.”

・Date/Time: December 10 (Tue.) 17:00‐18:30
・Venue: Middle Conference Room (2nd Floor), Building #1, Tohoku University School of Medicine
・Title: Topic of Bayesian Inference Using Positive Definite Kernel as the Most Recent Kernel Method
・Lecturer: Yu Nishiyama ( The Institute of Statistical Mathematics)

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: In recent studies regarding Kernel Method, Bayesian inference using positive definite Kernel (Kernel Bayes’ Rule) is examined. This methodology reasons with a kernel mean setting forth a consistency rate instead of probability distribution. The kernel mean is defined with an average in the feature space rather than regular average of probability distribution.  In this study, we will introduce kernelized version of both “Sum rule” used for marginalization and “Bayes’ rule” producing Bayesian Posterior Distribution known as “Kernel Sum Rule” and “Kernel Bayes’ Rules.” Combination of these two rules results in filtering of state special model and reinforcement learning algorithm. Kernel Bayes’ Rule which is the combined form of probability models performed by the presenters recently is also introduced.

・Organizer : Kaname Kojima, Masao Nagasaki

The 40th In Silico Megabank Research Seminar will be held on Friday, December 6.

This Time, we will be welcoming Dr. Osamu Komori, The Institute of Statistical Mathematics as our lecturer, and he will be speaking on “Discussion of Asymptotic Properties of Generalized t-statistics and Its Application to Actual Data Analysis .”

・Date/Time: December 6(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization

・Title:Discussion of Asymptotic Properties of Generalized t-statistics and Its Application to Actual Data Analysis
・Lecturer: Osamu Komori ( The Institute of Statistical Mathematics)

＊This lecture is transferable as a class in the medical research-related lecture course.

・Abstract: In recent years, search for variable quantities (makers) that are valid for high-dimensional data analysis used at settings such as clinical medicine or various diagnosis of illness has become more and more important. For analysis on high-dimensional continues variable such as data of gene expression levels, often t statistics and c statistics (AUC) are used at the phase narrowing down the variables. In this research, we focus on the t statistics, one of test statistics, and consider the application of t statistics to classification problems while taking account of multivariate linear combination instead of univariate. By considering U Function, a generating function to t statistics, we can discuss generalization of various t statistics. This research revealed close relation among t statistics, c statistics (AUC), Fisher Linear Discriminant, and Kullback-Libler divergence. In addition, the result suggests Lasso type method which L1 penalty is added to generalized t statistics as an example of application of this method to actual data analysis. One feature of this method is that selection of other variables is possible after fixing variables that are recognized as valid beforehand (such as variables that strong relation to risk of development). Its usability is to be examined with simulation and actual data analysis. In addition, we would like to consider applicable statistical method on discrete data such as SNP in the future.

・Organizer: Gen Tamiya, Masao Nagasaki

Access : http://www.megabank.tohoku.ac.jp/english/info/access.html