Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

被引:20
作者
Asim, Muhammad Nabeel [1 ,2 ]
Ibrahim, Muhammad Ali [1 ,2 ]
Malik, Muhammad Imran [3 ,4 ]
Dengel, Andreas [1 ,2 ]
Ahmed, Sheraz [1 ,5 ]
机构
[1] German Res Ctr Artificial Intelligence DFKI, D-67663 Kaiserslautern, Germany
[2] Tech Univ Kaiserslautern, Dept Comp Sci, D-67663 Kaiserslautern, Germany
[3] Natl Univ Sci & Technol, Natl Ctr Artificial Intelligence NCAI, Islamabad 44000, Pakistan
[4] Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, Islamabad 44000, Pakistan
[5] DeepReader GmbH, Trippstadter Str 122, D-67663 Kaiserslautern, Germany
关键词
non-coding RNA classification; RNA sub-cellular localization; long non-coding RNA; small non-coding RNA; ncRNA; machine learning; deep learning; computational sequence analysis; benchmark performance; benchmark sequence analysis datasets; POSTTRANSCRIPTIONAL GENE-REGULATION; RIBOSOMAL-RNA; CIRCULAR RNA; PROTEIN; LOCALIZATION; SEQUENCE; IDENTIFICATION; MICRORNA; DATABASE; PIWI;
D O I
10.3390/ijms22168719
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.
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页数:43
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