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Volume 7
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10.3390/ncrna7040077
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Review

Common Features in lncRNA Annotation and Classification: A Survey

by
Christopher Klapproth
1,
Rituparno Sen
2,
Peter F. Stadler
1,3,4,5,6,7,
Sven Findeiß
1 and
Jörg Fallmann
1,*
1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany
2
Helmholtz Institute for RNA-Based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), D-97080 Würzburg, Germany
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases, University Leipzig, D-04103 Leipzig, Germany
4
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany
5
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria
6
Facultad de Ciencias, Universidad National de Colombia, Bogotá CO-111321, Colombia
7
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
*
Author to whom correspondence should be addressed.
Non-Coding RNA 2021, 7(4), 77; https://doi.org/10.3390/ncrna7040077
Submission received: 12 November 2021 / Revised: 3 December 2021 / Accepted: 6 December 2021 / Published: 13 December 2021
(This article belongs to the Section Long Non-Coding RNA)
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Abstract

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.
Keywords: lncRNA; feature extraction; machine learning; coding sequence; classification problems lncRNA; feature extraction; machine learning; coding sequence; classification problems

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MDPI and ACS Style

Klapproth, C.; Sen, R.; Stadler, P.F.; Findeiß, S.; Fallmann, J. Common Features in lncRNA Annotation and Classification: A Survey. Non-Coding RNA 2021, 7, 77. https://doi.org/10.3390/ncrna7040077

AMA Style

Klapproth C, Sen R, Stadler PF, Findeiß S, Fallmann J. Common Features in lncRNA Annotation and Classification: A Survey. Non-Coding RNA. 2021; 7(4):77. https://doi.org/10.3390/ncrna7040077

Chicago/Turabian Style

Klapproth, Christopher, Rituparno Sen, Peter F. Stadler, Sven Findeiß, and Jörg Fallmann. 2021. "Common Features in lncRNA Annotation and Classification: A Survey" Non-Coding RNA 7, no. 4: 77. https://doi.org/10.3390/ncrna7040077

APA Style

Klapproth, C., Sen, R., Stadler, P. F., Findeiß, S., & Fallmann, J. (2021). Common Features in lncRNA Annotation and Classification: A Survey. Non-Coding RNA, 7(4), 77. https://doi.org/10.3390/ncrna7040077

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