Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

Author(s): Abbasali Emamjomeh*, Javad Zahiri, Mehrdad Asadian, Mehrdad Behmanesh, Barat A. Fakheri, Ghasem Mahdevar.

Journal Name: Medicinal Chemistry

Volume 15 , Issue 3 , 2019

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Graphical Abstract:


Background: Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

Objective: The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.

Method: In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

Results: The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

Conclusion: ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Keywords: ncRNAs, drug design, experimental methods, algorithm, database, tool.

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Year: 2019
Page: [216 - 230]
Pages: 15
DOI: 10.2174/1573406414666181015151610
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