Title |
Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA–protein binding sites
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Published in |
Genome Biology, September 2017
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DOI | 10.1186/s13059-017-1298-8 |
Pubmed ID | |
Authors |
Yang Eric Li, Mu Xiao, Binbin Shi, Yu-Cheng T. Yang, Dong Wang, Fei Wang, Marco Marcia, Zhi John Lu |
Abstract |
Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements. |
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Geographical breakdown
Country | Count | As % |
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Switzerland | 1 | 11% |
Germany | 1 | 11% |
United States | 1 | 11% |
France | 1 | 11% |
United Kingdom | 1 | 11% |
Unknown | 4 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 67% |
Members of the public | 2 | 22% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 125 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 42 | 34% |
Researcher | 23 | 18% |
Student > Master | 7 | 6% |
Student > Bachelor | 7 | 6% |
Professor > Associate Professor | 6 | 5% |
Other | 19 | 15% |
Unknown | 21 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 48 | 38% |
Agricultural and Biological Sciences | 29 | 23% |
Computer Science | 16 | 13% |
Medicine and Dentistry | 2 | 2% |
Neuroscience | 2 | 2% |
Other | 3 | 2% |
Unknown | 25 | 20% |