Title |
A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing
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Published in |
BMC Genomics, January 2013
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DOI | 10.1186/1471-2164-14-s1-s2 |
Pubmed ID | |
Authors |
Chih-Hung Chou, Feng-Mao Lin, Min-Te Chou, Sheng-Da Hsu, Tzu-Hao Chang, Shun-Long Weng, Sirjana Shrestha, Chiung-Chih Hsiao, Jui-Hung Hung, Hsien-Da Huang |
Abstract |
MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need. |
X Demographics
Geographical breakdown
Country | Count | As % |
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France | 1 | 25% |
Unknown | 3 | 75% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 1 | <1% |
France | 1 | <1% |
India | 1 | <1% |
United Kingdom | 1 | <1% |
Denmark | 1 | <1% |
Korea, Republic of | 1 | <1% |
United States | 1 | <1% |
Poland | 1 | <1% |
Unknown | 102 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 33 | 30% |
Researcher | 25 | 23% |
Student > Master | 13 | 12% |
Student > Bachelor | 9 | 8% |
Professor | 7 | 6% |
Other | 14 | 13% |
Unknown | 9 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 49 | 45% |
Biochemistry, Genetics and Molecular Biology | 21 | 19% |
Computer Science | 13 | 12% |
Engineering | 4 | 4% |
Medicine and Dentistry | 2 | 2% |
Other | 5 | 5% |
Unknown | 16 | 15% |