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Author Spotlight: RNA FISH for Locating lncRNA-SNHG6 in Osteosarcoma Cells

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Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.

Muhammad Nabeel Asim^1,2, Muhammad Ali Ibrahim^1,2, Muhammad Imran Malik^3,4

¹German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany.

International Journal of Molecular Sciences

|August 27, 2021

Summary

This study reviews computational methods for analyzing non-coding RNAs (ncRNAs), focusing on classification, sub-cellular localization, and their roles in diseases like cancer. It aids AI researchers in model selection and understanding RNA sequence analysis challenges.

Keywords:

RNA sub-cellular localization benchmark performance benchmark sequence analysis datasets computational sequence analysis deep learning long non-coding RNA machine learning ncRNA non-coding RNA classification small non-coding RNA

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Non-coding RNAs (ncRNAs) play critical roles in cellular processes and disease development, including cancer.
Understanding ncRNA function requires accurate classification and sub-cellular localization.
High-throughput sequencing and bioinformatics have accelerated ncRNA research.

Purpose of the Study:

To review computational methodologies for distinguishing coding RNA from ncRNA.
To identify various ncRNA subtypes, including microRNA, long ncRNA, and circular RNA.
To determine the sub-cellular localization of ncRNAs and assess computational approaches for RNA sequence analysis.

Main Methods:

Literature review of computational approaches developed in the last 10 years.
Analysis of datasets for ncRNA classification and sub-cellular localization.
Benchmarking of existing computational methodologies for RNA sequence analysis.

Main Results:

Identification of diverse ncRNA types and their involvement in cellular regulation and disease.
Summary of computational tools and techniques for ncRNA identification and localization.
Evaluation of performance metrics and datasets for computational RNA analysis.

Conclusions:

Computational approaches are crucial for advancing ncRNA research and understanding disease mechanisms.
Further development is needed to address research gaps and challenges in RNA sequence analysis.
This review provides insights for AI researchers on state-of-the-art methods and model selection for ncRNA-related tasks.