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Computational Prediction of Gene Regulation by lncRNAs
Tanvi Sinha1, Susovan Sadhukhan1, Amaresh C Panda2
1Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha, India.
Methods in Molecular Biology (Clifton, N.J.)
|December 20, 2024
Summary
Most of the genome transcribes into RNA, but only a small fraction codes for proteins. This summary focuses on long noncoding RNAs (lncRNAs) and the computational tools used to analyze their functions.
Area of Science:
- Genomics and Molecular Biology
- Bioinformatics and Computational Biology
Background:
- High-throughput sequencing reveals extensive genome transcription into RNA.
- A significant portion of cellular RNA comprises noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs).
- lncRNAs are critical regulators of gene expression through interactions with RNA, DNA, and proteins.
Purpose of the Study:
- To discuss available tools for lncRNA functional analysis.
- To provide a detailed workflow for the computational analysis of lncRNAs.
Main Methods:
- Review of existing databases and computational tools for lncRNA research.
- Description of a comprehensive computational analysis workflow for lncRNAs.
Main Results:
- Identification of key bioinformatics tools for lncRNA functional exploration.
- Outline of a step-by-step computational approach for analyzing lncRNA functions.
Conclusions:
- lncRNAs play a vital role in gene regulation.
- Computational tools and defined workflows are essential for understanding lncRNA functions in cellular physiology.
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