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Denis Jacob Machado
Mar 10, 2014, 1:51:54 PM3/10/14
to sequ...@googlegroups.com
Palestrante: Rolf Backofen, Universität Freiburg, Alemanha
http://www.bioinf.uni-freiburg.de/~backofen/
Quando: 14/3/2014, 14:30hs
Onde: Instituto de Química da USP, Anfiteatro Cinza, bloco 6 superior
Título: How to determine binding motifs for RNA-binding proteins
Resumo:
It is becoming increasingly clear that a RNA-binding proteins are key
elements in regulating the cell's transcriptome. Thus, unraveling the
interaction network of the RNA-binding proteins by determining their
binding sites is becoming an increasingly important topic. There are
several high-throughput methods available to detect binding sites such
as CLIP-seq. Since not all possible binding
sites are covered due to differential expression in tissues and
developmental states, the main problem is to come up with good motif
descriptions to find missing binding sites and to evaluate the binding
strength.
It is well know that binding motifs for RNA-binding proteins have to
contain both sequential and structural information. Previous
approaches used only structural profiles. Our new approach GraphProt
uses an advanced machine learning approach based on our graph-kernel,
and is able to use both structural profiles as well as detailed
2D-structures, without the need to decide a priori about the weight of
the different structural components. In addition, we are able to
integrate also context information. Furthermore, we provide support for
mutagenesis analysis, which is a non-trivial task, provided that
structural information is used.
Promoção do Núcleo Pesquisa em Ciências Genômicas
http://lbi.iq.usp.br/napcg
http://www.bioinf.uni-freiburg.de/~backofen/
Quando: 14/3/2014, 14:30hs
Onde: Instituto de Química da USP, Anfiteatro Cinza, bloco 6 superior
Título: How to determine binding motifs for RNA-binding proteins
Resumo:
It is becoming increasingly clear that a RNA-binding proteins are key
elements in regulating the cell's transcriptome. Thus, unraveling the
interaction network of the RNA-binding proteins by determining their
binding sites is becoming an increasingly important topic. There are
several high-throughput methods available to detect binding sites such
as CLIP-seq. Since not all possible binding
sites are covered due to differential expression in tissues and
developmental states, the main problem is to come up with good motif
descriptions to find missing binding sites and to evaluate the binding
strength.
It is well know that binding motifs for RNA-binding proteins have to
contain both sequential and structural information. Previous
approaches used only structural profiles. Our new approach GraphProt
uses an advanced machine learning approach based on our graph-kernel,
and is able to use both structural profiles as well as detailed
2D-structures, without the need to decide a priori about the weight of
the different structural components. In addition, we are able to
integrate also context information. Furthermore, we provide support for
mutagenesis analysis, which is a non-trivial task, provided that
structural information is used.
Promoção do Núcleo Pesquisa em Ciências Genômicas
http://lbi.iq.usp.br/napcg
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