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DEEPLEARNRBP SIGNED

Using Deep Learning to understand RNA Binding Protein binding characteristics

Total Cost €

EC-Contrib. €

Partnership

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DEEPLEARNRBP project word cloud

Explore the words cloud of the DEEPLEARNRBP project. It provides you a very rough idea of what is the project "DEEPLEARNRBP" about.

implications theoretical standalone new enigmatic interpret rna immunoprecipitation dynamic rbps bioinformatics input sites hundreds motifs secondary clip termed substrate biological fashion expertise combinations unknown interdisciplinary close data transfer conservation discernable protein transplanting experimentation structure filters biology binding bind sequencing functional crosslinking harnessed disseminate web sequence global rbp involve interplay pulled torrent function nature screens deep practical enigmrbps machine varied proteins model techniques technologies explore revolutionized format domain fertile environment learning seq practically

Project "DEEPLEARNRBP" data sheet

The following table provides information about the project.

Coordinator	Masarykova univerzita Organization address address: Zerotinovo namesti 9 city: BRNO STRED postcode: 60177 website: http://www.muni.cz contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Czech Republic [CZ]
Total cost	156˙980 €
EC max contribution	156˙980 € (100%)
Programme	1. H2020-EU.4. (SPREADING EXCELLENCE AND WIDENING PARTICIPATION)
Code Call	H2020-WF-01-2018
Funding Scheme	MSCA-IF-EF-RI
Starting year	2019
Duration (year-month-day)	from 2019-07-01 to 2022-05-09

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	Masarykova univerzita Masarykova univerzita Organization address address: Zerotinovo namesti 9 city: BRNO STRED postcode: 60177 website: http://www.muni.cz contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	CZ (BRNO STRED)	coordinator	156˙980.00

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Project objective

New technologies have revolutionized our understanding of RNA binding protein (RBP) function. Global screens for RBPs have pulled down hundreds of proteins for which no discernable RNA Binding Domain is present. These proteins, termed enigmRBPs due to their enigmatic nature, do bind RNA in unknown and variable fashion. An ever increasing number of such RBPs are having their target sites identified via CrossLinking and ImmunoPrecipitation Sequencing techniques (CLIP-Seq). This torrent of data can be harnessed by novel Deep Learning techniques to identify high order characteristics of RBP function.

The aim of this proposal is the development of a machine learning model that can explore the functional implications of RBP binding characteristics. A model that, given an enigmatic RBP, can identify other known RBPs that show similar binding characteristics, such as sequence motifs, conservation motifs, secondary structure motifs, and higher order combinations of the above.

We will focus on methods to practically interpret the machine learning model to biological knowledge, especially higher order filters that can learn the interplay among varied input, such as secondary structure, sequence and conservation. Beyond the theoretical, we will disseminate our methods in easy to use, standalone and web application format, in order to increase the practical application of our research.

We are transplanting expertise from the bioinformatics and machine learning field, into a fertile substrate of RNA biology and CLIP-Seq experimentation. This interdisciplinary project will involve close collaboration and two-way transfer of knowledge in a dynamic research environment.

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The information about "DEEPLEARNRBP" are provided by the European Opendata Portal: CORDIS opendata.