Chapter title |
RNA Function Prediction
|
---|---|
Chapter number | 2 |
Book title |
Functional Genomics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7231-9_2 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7230-2, 978-1-4939-7231-9
|
Authors |
Yongsheng Li, Juan Xu, Tingting Shao, Yunpeng Zhang, Hong Chen, Xia Li |
Abstract |
Recent studies have shown that a considerable proportion of eukaryotic genomes are transcribed as noncoding RNA (ncRNA), and regulatory ncRNAs have attracted much attention from researchers in many fields, especially of microRNA (miRNA) and long noncoding RNA (lncRNA). However, most ncRNAs are functionally uncharacterized due to the difficulty to accurately identify their targets. In this chapter, we first summarize the most recent advances in ncRNA research and their primary function. We then discuss the current state-of-the-art computational methods for predicting RNA functions, which comprise three different categories: miRNA function prediction approaches using target genes, lncRNA function prediction based on the guilt-by-association principle, and RNA function prediction approaches based on competing endogenous RNA partners. We consider that the application of these techniques can provide valuable functional and mechanistic insights into ncRNAs, and that they are crucial steps in future functional studies. |
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Mendeley readers
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Demographic breakdown
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Student > Master | 3 | 19% |
Student > Doctoral Student | 2 | 13% |
Student > Bachelor | 1 | 6% |
Unspecified | 1 | 6% |
Other | 2 | 13% |
Unknown | 3 | 19% |
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Unspecified | 1 | 6% |
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Immunology and Microbiology | 1 | 6% |
Other | 1 | 6% |
Unknown | 3 | 19% |