A Hitchhiker's guide to RNA–RNA structure and interaction prediction tools

Author:

Tieng Francis Yew Fu1ORCID,Abdullah-Zawawi Muhammad-Redha1,Md Shahri Nur Alyaa Afifah1,Mohamed-Hussein Zeti-Azura23,Lee Learn-Han456,Mutalib Nurul-Syakima Ab1567ORCID

Affiliation:

1. UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM) , Kuala Lumpur 56000 , Malaysia

2. Institute of Systems Biology (INBIOSIS) , UKM, Selangor 43600 , Malaysia

3. Department of Applied Physics, Faculty of Science and Technology, UKM , Selangor 43600 , Malaysia

4. Sunway Microbiomics Centre, School of Medical and Life Sciences, Sunway University , Sunway City 47500 , Malaysia

5. Novel Bacteria and Drug Discovery Research Group , Microbiome and Bioresource Research Strength, , Selangor 47500 , Malaysia

6. Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia , Microbiome and Bioresource Research Strength, , Selangor 47500 , Malaysia

7. Faculty of Health Sciences, UKM , Kuala Lumpur 50300 , Malaysia

Abstract

Abstract RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA–RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA–RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA–RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Funder

Universiti Kebangsaan Malaysia and Ministry of Higher Education

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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