Title |
PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data
|
---|---|
Published in |
Genome Biology, August 2011
|
DOI | 10.1186/gb-2011-12-8-r79 |
Pubmed ID | |
Authors |
David L Corcoran, Stoyan Georgiev, Neelanjan Mukherjee, Eva Gottwein, Rebecca L Skalsky, Jack D Keene, Uwe Ohler |
Abstract |
Crosslinking and immunoprecipitation (CLIP) protocols have made it possible to identify transcriptome-wide RNA-protein interaction sites. In particular, PAR-CLIP utilizes a photoactivatable nucleoside for more efficient crosslinking. We present an approach, centered on the novel PARalyzer tool, for mapping high-confidence sites from PAR-CLIP deep-sequencing data. We show that PARalyzer delineates sites with a high signal-to-noise ratio. Motif finding identifies the sequence preferences of RNA-binding proteins, as well as seed-matches for highly expressed microRNAs when profiling Argonaute proteins. Our study describes tailored analytical methods and provides guidelines for future efforts to utilize high-throughput sequencing in RNA biology. PARalyzer is available at http://www.genome.duke.edu/labs/ohler/research/PARalyzer/. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 50% |
France | 1 | 25% |
Australia | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 50% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 2% |
France | 4 | 1% |
Germany | 4 | 1% |
Italy | 3 | <1% |
United Kingdom | 2 | <1% |
Czechia | 1 | <1% |
Sweden | 1 | <1% |
Austria | 1 | <1% |
China | 1 | <1% |
Other | 3 | <1% |
Unknown | 298 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 111 | 34% |
Researcher | 69 | 21% |
Student > Master | 33 | 10% |
Student > Bachelor | 17 | 5% |
Student > Doctoral Student | 14 | 4% |
Other | 46 | 14% |
Unknown | 36 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 157 | 48% |
Biochemistry, Genetics and Molecular Biology | 69 | 21% |
Computer Science | 27 | 8% |
Medicine and Dentistry | 9 | 3% |
Immunology and Microbiology | 5 | 2% |
Other | 16 | 5% |
Unknown | 43 | 13% |