EPIGENE INFORMATICS

Machine learning approaches to epigenomic research

Coordinatore	THE UNIVERSITY OF EDINBURGH    more  Organization address address: OLD COLLEGE, SOUTH BRIDGE city: EDINBURGH postcode: EH8 9YL contact info Titolo: Mr. Nome: Gordon Cognome: Marshall Email: send email Telefono: +44 131 651 4386 Fax: +44 131 651 4028
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	209˙033 €
EC contributo	209˙033 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2011-IEF
Funding Scheme	MC-IEF
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-05-16   -   2015-04-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	THE UNIVERSITY OF EDINBURGH  Organization address address: OLD COLLEGE, SOUTH BRIDGE city: EDINBURGH postcode: EH8 9YL contact info Titolo: Mr. Nome: Gordon Cognome: Marshall Email: send email Telefono: +44 131 651 4386 Fax: +44 131 651 4028	UK (EDINBURGH)	coordinator	209˙033.40

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 Obiettivo del progetto (Objective)

'Epigenomic research has become one the fastest evolving fields in molecular biology. Epigenetic effects control the packaging of DNA in the nucleus thereby deeply influencing gene expression. They also play crucial roles in cell differentiation and aberrant patterns are associated with cancer, mental disorders and autoimmune diseases. However, our understanding of the epigenomic code is still limited. A main obstacle to its decoding is the requirement for immensely data-intense experiments, as epigenomic configurations embrace multiple different marks which form an intricate interplay that varies between cell types. Advances in high-throughput sequencing resulted in a plethora of complex data sets and computational methods are called upon to solve pressing questions for their analysis and modeling. In this project, we will develop machine learning tools to combine epigenomic measurements with computational sequence analysis. This will provide us with a better understanding of the extent to which DNA sequence controls the establishment of epigenomic marks. It will also serve as a credible basis for data integration. We will next combine data from different cell lines and analyze them simultaneously. In particular, we will examine the effect of a certain transfactor, by comparing histone marks in wild type ES cells with mutants that lack the DNA-binding protein Cfp-1, which is known to play a role in the formation of epigenomic marks at active promoters. To shed light on the epigenomic impact on the transcriptome the framework will eventually be complemented by adding expression data. The proposed research will doubly benefit the applicant by introducing her to a new field of application, as well as a wider class of computational techniques. We believe this work to be of scientific importance, as the employed machine learning approaches are likely to lead to new insights in epigenome research with immense potential consequences in addressing key biomedical issues.'