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Review

A Survey of Current Resources to Study lncRNA-Protein Interactions

by 1, 1 and 1,2,3,*
1
School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia
2
Monash eResearch Centre, Monash University, Clayton, VIC 3800, Australia
3
Department of Infectious Disease, Monash University (Alfred Campus), 85 Commercial Road, Melbourne, VIC 3004, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Y-h. Taguchi
Non-Coding RNA 2021, 7(2), 33; https://doi.org/10.3390/ncrna7020033
Received: 3 May 2021 / Revised: 28 May 2021 / Accepted: 7 June 2021 / Published: 8 June 2021
Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein–DNA interactions such as histone and transcription factor binding are well studied, along with RNA–RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms. View Full-Text
Keywords: LPI; lncRNA; ncRNA; protein; transcriptomics; molecular docking; machine learning; deep learning; databases LPI; lncRNA; ncRNA; protein; transcriptomics; molecular docking; machine learning; deep learning; databases
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MDPI and ACS Style

Philip, M.; Chen, T.; Tyagi, S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Non-Coding RNA 2021, 7, 33. https://doi.org/10.3390/ncrna7020033

AMA Style

Philip M, Chen T, Tyagi S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Non-Coding RNA. 2021; 7(2):33. https://doi.org/10.3390/ncrna7020033

Chicago/Turabian Style

Philip, Melcy, Tyrone Chen, and Sonika Tyagi. 2021. "A Survey of Current Resources to Study lncRNA-Protein Interactions" Non-Coding RNA 7, no. 2: 33. https://doi.org/10.3390/ncrna7020033

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