TRFolder-W: a web server for telomerase RNA structure prediction in yeast genomes

Area of Science:

• Computational biology
• RNA structure prediction

Background:

• Telomerase RNA (TR) plays a crucial role in telomere maintenance.
• Five core sub-structures are commonly identified in fungal TR.
• Previous work established the TRFolder Python toolkit for TR structure prediction.

Purpose of the Study:

• To develop a user-friendly web server (TRFolder-W) for predicting telomerase RNA core structures.
• To enhance the flexibility and accessibility of TR structure prediction for yeast genomes.
• To provide a tool for identifying potential TR genes based on conserved structural units.

Main Methods:

• Development of a web server using the Django web framework.
• Leveraging the existing TRFolder command-line toolkit.
• Integration of five core TR sub-structures (template region, downstream pseudoknot, boundary element, core-closing stem, triple helix) for prediction.

Main Results:

• TRFolder-W offers a flexible and user-friendly interface for TR structure prediction.
• The server facilitates the prediction of potential TR genes in yeast.
• The methodology can be extended to predict TR structures in non-fungal species.

Conclusions:

• TRFolder-W provides a valuable resource for yeast telomerase RNA structure analysis.
• The web server enhances the prediction of core TR structures, aiding in gene identification.
• The tool's applicability extends beyond fungal species, offering broader potential in RNA research.