The 26th In Silico Megabank Research Seminar will be held on Friday, April 19.
This Time, we will be welcoming Mr. George Chalkidis, Insititute of Medical Science, The University of Tokyo as our lecturer, and he will be speaking on “Information Theoretic Analysis of Long non-coding RNA as a gene expression
control mechanism.”

・Date/Time: April 19(Fri.) 17:00‐18:30
・Venue: Conference Room 1(2nd Floor), Tohoku Medical Megabank Organization
http://www.megabank.tohoku.ac.jp/english/info/access.html

・Title: Information Theoretic Analysis of Long non-coding RNA as a gene expression
control mechanism

・Lecturer: George Chalkidis (Institute of Medical Science, The University of Tokyo)

・Abstract:
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.

・Organizer: Masao Nagasaki