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AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected
Dario Ruiz-Ciancio1,2,3, Suresh Veeramani4,5, Rahul Singh6
1Instituto de Ciencias Biomédicas (ICBM), Facultad de Ciencias Médicas, Universidad Católica de Cuyo, Av. José Ignacio de la Roza 1516, Rivadavia 5400, San Juan, Argentina.
Molecular Therapy. Nucleic Acids
|November 7, 2024
Summary
AptamerRunner is a new algorithm that analyzes next-generation sequencing data to identify promising aptamers. It integrates computation and visualization to improve the selection of validated aptamers.
Area of Science:
- Biotechnology
- Bioinformatics
- Molecular Biology
Background:
- Aptamers are DNA/RNA molecules with high target affinity, selected via SELEX.
- Next-generation sequencing (NGS) enhances aptamer analysis compared to Sanger sequencing.
- Identifying optimal aptamer candidates from NGS data remains a challenge.
Purpose of the Study:
- To present AptamerRunner, a novel algorithm for aptamer sequence and structure clustering.
- To integrate computational analysis with visualization for improved aptamer selection.
- To facilitate the identification of high-likelihood candidate aptamers for experimental validation.
Main Methods:
- Developed AptamerRunner, a clustering algorithm for aptamer NGS datasets.
- Integrated computational analysis with visualization tools.
- Incorporated expertise-directed decision-making into the workflow.
Main Results:
- AptamerRunner provides a visual context for networked aptamers and ranking data.
- The algorithm facilitates the selection of candidate aptamers based on fold enrichment and scoring.
- Offers a significant advancement over existing aptamer clustering tools.
Conclusions:
- AptamerRunner enhances the identification of validated aptamers from NGS data.
- The tool's flexibility and user-friendly design benefit aptamer researchers.
- Future enhancements promise broader applications across disciplines.

