Title |
GraphProt: modeling binding preferences of RNA-binding proteins
|
---|---|
Published in |
Genome Biology, January 2014
|
DOI | 10.1186/gb-2014-15-1-r17 |
Pubmed ID | |
Authors |
Daniel Maticzka, Sita J Lange, Fabrizio Costa, Rolf Backofen |
Abstract |
We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential to predict RBP-binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt. |
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United Kingdom | 2 | 20% |
United States | 2 | 20% |
China | 1 | 10% |
France | 1 | 10% |
Australia | 1 | 10% |
Unknown | 3 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 60% |
Scientists | 3 | 30% |
Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 3 | 1% |
Italy | 1 | <1% |
Sweden | 1 | <1% |
France | 1 | <1% |
Denmark | 1 | <1% |
Canada | 1 | <1% |
Spain | 1 | <1% |
China | 1 | <1% |
Other | 0 | 0% |
Unknown | 215 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 46 | 20% |
Student > Master | 34 | 15% |
Student > Bachelor | 14 | 6% |
Student > Postgraduate | 11 | 5% |
Other | 28 | 12% |
Unknown | 25 | 11% |
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Biochemistry, Genetics and Molecular Biology | 61 | 27% |
Computer Science | 37 | 16% |
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Engineering | 5 | 2% |
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Unknown | 32 | 14% |