[Mitarbeiter.zoologie] Fwd: Institutskolloquium am 20. Juni 2019

[Robert Paxton]

Dear All,

For all that might be interested in gene regulation,

Robert


> Begin forwarded message:
> 
> From: Ivo Grosse <grosse at informatik.uni-halle.de>
> Subject: Fwd: Institutskolloquium am 20. Juni 2019
> Date: 15 June 2019 at 14:41:37 CEST
> To: Sven-Erik Behrens <sven.behrens at biochemtech.uni-halle.de>, Robert Paxton <robert.paxton at zoologie.uni-halle.de>, Wolfgang Sippl <wolfgang.sippl at pharmazie.uni-halle.de>, Christiane Tammer <christiane.tammer at mathematik.uni-halle.de>, Wolfgang Paul <wolfgang.paul at physik.uni-halle.de>, Michael Bron <michael.bron at chemie.uni-halle.de>, Marcel Quint <marcel.quint at landw.uni-halle.de>, Gabriele Stangl <gabriele.stangl at landw.uni-halle.de>, Christine Fürst <christine.fuerst at geo.uni-halle.de>, Steffen Neumann <sneumann at ipb-halle.de>, Jens Freitag <freitagj at ipk-gatersleben.de>, François Buscot <francois.buscot at ufz.de>
> 
> Dear colleagues,
> 
> could you please forward this announcement to potentially interested people at your institutes?
> 
> Have a great weekend and a great week,
> 
> Ivo
> 
>> Begin forwarded message:
>> 
>> From: Ivo Grosse <grosse at informatik.uni-halle.de <mailto:grosse at informatik.uni-halle.de>>
>> Subject: Institutskolloquium am 20. Juni 2019
>> Date: 15. June 2019 at 14:23:38 CEST
>> To: Umlauf Informatik <umlauf at informatik.uni-halle.de <mailto:umlauf at informatik.uni-halle.de>>
>> Cc: Fachschaftsrat <fachschaft at mathinf.uni-halle.de <mailto:fachschaft at mathinf.uni-halle.de>>
>> 
>> Im Rahmen eines
>> 
>> Kolloquiums
>> des Instituts für Informatik
>> 
>> hält
>> 
>> Herr Prof. Dr. Matthew T. Weirauch
>> (University of Cincinnati and Cincinnati Children’s Hospital, Cincinnati, Ohio, USA)
>> 
>> einen Vortrag zum Thema:
>> 
>> Computational Approaches for Understanding Gene Regulation Across Life
>> 
>> Der Vortrag findet am Donnerstag, dem 20. Juni 2019, um 16 Uhr c.t. im Hörsaal 3.31, Von-Seckendorff-Platz 1, statt.
>> 
>> Abstract
>> 
>> Gene regulation is the carefully orchestrated process controlling the expression of all of the genes in all of the cells of all living organisms.  Gene regulation is highly complex and operates on multiple scales, making it an ideal topic to study using computational approaches such as machine learning.  The complexity, scale, and nuances of biology also create unique computational challenges.  In this lecture, I will present the results of multiple large-scale computational projects aimed at understanding gene regulation on a global scale.
>> 
>> Transcription factor proteins, which are key components of gene regulatory systems, work by binding to specific DNA sequences in the genome to regulate gene expression.  I will provide a broad overview of the hundreds of thousands of transcription factors across all forms of life.  Evolution has produced a stunning variety of these proteins, and they play key roles in virtually every known biological process.  Although DNA sequence binding preferences direct the regulatory activity of transcription factors, they are currently known for only a small fraction of all transcription factors.
>> 
>> Broadly sampling transcription factor families across the tree of life using novel computational approaches, we have experimentally determined DNA sequence binding preferences for >2,000 transcription factors encompassing over 50 different families from hundreds of diverse eukaryotes.  Using a novel approach we call similarity regression, we have learned rules that can be applied to map this information across different species.
>> 
>> Similarity regression is formulated as a regression task where the dependent variable (Y) is a metric of similarity in DNA sequence specificity between pairs of proteins, and the independent variables (the feature vector X) are similarity in amino acid residues at each individual position of the aligned proteins.  Collectively, these data form the basis of our widely used Cis-BP database, a relational database consisting of 21 tables and over 100 million entries.
>> 
>> I will demonstrate the application of these data for the interpretation of gene regulatory mechanisms and predicting the functional impact of disease-associated non-coding genetic variants.  I will also highlight and discuss how modern machine learning approaches such as deep learning are being used for understanding the roles played by gene regulation in human disease.
>> 
>> *********
>> 
>> Alle Interessenten sind herzlich eingeladen.
>> 
>> Mit freundlichen Grüßen
>> 
>> Prof. Dr. Ivo Große


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