Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Survey of Current Resources to Study lncRNA-protein Interactions.

Version 1 : Received: 10 May 2021 / Approved: 11 May 2021 / Online: 11 May 2021 (10:54:27 CEST)

A peer-reviewed article of this Preprint also exists.

Philip, M.; Chen, T.; Tyagi, S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Non-Coding RNA 2021, 7, 33. Philip, M.; Chen, T.; Tyagi, S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Non-Coding RNA 2021, 7, 33.


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 driven by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanism, 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. 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.


LPI, lncRNA, ncRNA, protein, transcriptomics, molecular docking, machine learning, deep learning, databases


Biology and Life Sciences, Biochemistry and Molecular Biology

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