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Common Features in lncRNA Annotation and Classification: A Survey.

Christopher Klapproth¹, Rituparno Sen², Peter F Stadler^1,3,4,5,6,7

¹Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany.

|December 23, 2021

Summary

Computational tools for identifying long non-coding RNAs (lncRNAs) excel at distinguishing them from protein-coding sequences. However, differentiating lncRNAs from other non-coding RNAs remains a significant challenge.

Keywords:

classification problems coding sequence feature extraction lncRNA machine learning

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Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Long non-coding RNAs (lncRNAs) are critical gene expression regulators with diverse functions.
Many lncRNAs are poorly characterized, hindering their potential as diagnostic and therapeutic targets.
Understanding lncRNA annotation is crucial for advancing molecular biology research.

Purpose of the Study:

To review and analyze in silico approaches for lncRNA annotation.
To evaluate the strengths and weaknesses of current computational methods for lncRNA identification.
To identify key research gaps in lncRNA classification.

Main Methods:

Survey of established in silico methods for lncRNA annotation.
Analysis of features used in lncRNA classification algorithms.
Comparative assessment of tool performance in distinguishing lncRNAs from other RNA types.

Main Results:

Current computational tools effectively differentiate coding sequences from other RNAs.
Existing methods struggle to distinguish lncRNAs from other non-protein-coding sequences.
A notable limitation is the difficulty in distinguishing lncRNAs from intronic sequences and UTRs.

Conclusions:

While computational tools are adept at basic RNA classification, they fall short in precise lncRNA identification.
Distinguishing lncRNAs from intronic and untranslated regions represents a critical unmet need in bioinformatics.
Further development of in silico methods is required to fully characterize the lncRNA landscape.