| Title |
Discovery of Novel MicroRNAs in Rat Kidney Using Next Generation Sequencing and Microarray Validation
|
|---|---|
| Published in |
PLOS ONE, March 2012
|
| DOI | 10.1371/journal.pone.0034394 |
| Pubmed ID | |
| Authors |
Fanxue Meng, Michael Hackenberg, Zhiguang Li, Jian Yan, Tao Chen |
| Abstract |
MicroRNAs (miRNAs) are small non-coding RNAs that regulate a variety of biological processes. The latest version of the miRBase database (Release 18) includes 1,157 mouse and 680 rat mature miRNAs. Only one new rat mature miRNA was added to the rat miRNA database from version 16 to version 18 of miRBase, suggesting that many rat miRNAs remain to be discovered. Given the importance of rat as a model organism, discovery of the completed set of rat miRNAs is necessary for understanding rat miRNA regulation. In this study, next generation sequencing (NGS), microarray analysis and bioinformatics technologies were applied to discover novel miRNAs in rat kidneys. MiRanalyzer was utilized to analyze the sequences of the small RNAs generated from NGS analysis of rat kidney samples. Hundreds of novel miRNA candidates were examined according to the mappings of their reads to the rat genome, presence of sequences that can form a miRNA hairpin structure around the mapped locations, Dicer cleavage patterns, and the levels of their expression determined by both NGS and microarray analyses. Nine novel rat hairpin precursor miRNAs (pre-miRNA) were discovered with high confidence. Five of the novel pre-miRNAs are also reported in other species while four of them are rat specific. In summary, 9 novel pre-miRNAs (14 novel mature miRNAs) were identified via combination of NGS, microarray and bioinformatics high-throughput technologies. |
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|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Malaysia | 1 | 2% |
| Unknown | 50 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 28% |
| Researcher | 12 | 23% |
| Student > Bachelor | 4 | 8% |
| Professor > Associate Professor | 4 | 8% |
| Other | 3 | 6% |
| Other | 7 | 13% |
| Unknown | 8 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 25 | 47% |
| Medicine and Dentistry | 6 | 11% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Computer Science | 3 | 6% |
| Engineering | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 10 | 19% |