Computational Resources for lncRNA Functions and Targetome

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.