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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

Computational Resources for lncRNA Functions and Targetome.

Anamika Thakur^1,2, Manoj Kumar^3,4

¹Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh, India.

Methods in Molecular Biology (Clifton, N.J.)

|December 20, 2024

Summary

This summary is machine-generated.

This review summarizes computational tools and databases for studying long non-coding RNAs (lncRNAs). These resources aid researchers in understanding lncRNA functions and their roles in various diseases.

Keywords:

Algorithm Analysis tools Database Machine learning lncRNA

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Long non-coding RNAs (lncRNAs) are key regulators in biological processes.
Dysregulated lncRNA expression is linked to various diseases, indicating therapeutic potential.
Numerous computational resources for lncRNA research have emerged.

Purpose of the Study:

To provide a comprehensive review of existing lncRNA databases and predictive tools.
To highlight the importance of lncRNA resources across different organisms, including humans and model organisms.
To guide biologists in selecting appropriate computational tools for their research.

Main Methods:

Review of lncRNA databases and registries.
Analysis of machine learning algorithms (Deep Learning, SVM, RF) for lncRNA identification.
Compilation of resources for lncRNA expression, disease association, and target regulation.

Main Results:

Detailed overview of key lncRNA databases and updated resources.
Discussion of computational tools for identifying lncRNAs using various techniques.
Categorization of resources based on data types like differential expression and target interactions.

Conclusions:

In silico resources are vital for advancing lncRNA research.
This review empowers biologists with knowledge of available computational tools.
Understanding lncRNAs through computational approaches is crucial for biological insights and disease research.