2013.04.11

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第26回インシリコ・メガバンク研究会開催のお知らせ（4月19日）

第26回インシリコ・メガバンク研究会を下記のとおり行いますのでご案内いたします。
今回は東京大学医科学研究所 George Chalkidisさんを講師としてお迎えし、「遺伝子発現制御機序としての長鎖非コードRNAの情報理論分析」について講演していただきます。

日時：平成25年4月19日(金)　17：00‐18：30
場所：東北メディカル・メガバンク機構2階会議室1

演題：Information Theoretic Analysis of Long non-coding RNA as a gene expression control mechanism
講師：Dr. George Chalkidis ( Institute of Medical Science, The University of Tokyo)

概要：An emerging theme from multiple model systems is that lncRNAs form extensive networks of regulatory mechanisms that control the higher-order organization of the transcriptome. The importance of these modes of regulation is underscored by the newly recognized roles of long non-coding RNAs for proper gene control across all kingdoms of life.
The means by which such lncRNAs regulate transcription are speculated to encompass a diversity of mechanisms and the identification and characterization of these mechanisms is an active area of research.
There will be 30-50 RNA-seq and TSS-seq data samples from human cultured cells from various cell lines and various conditions. The objective will be to discover potential control mechanisms and model them from small, noisy samples. The Minimum Description Length Principle (MDL) together with the maximal information coefficient (MIC) are particularly suited for that task, because MDL shows excellent non- asymptotical performance in terms of learning the statistics of finite size context sources from finite length training data and thus enables the de-noising and correct model identification even from small sample sizes. Furthermore, MIC is a powerful novel dependency measure for two-variable relationships of the class of maximal information-based nonparametric exploration statistics that captures a wide range of associations both functional and non-functional and provides a score close to 1 for functional and non-functional relationships and score close to 0 for statistically independent random variables.

世話人：長﨑正朗