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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, Eric Embree6
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.
Biorxiv : the Preprint Server for Biology
|November 28, 2023
Summary
AptamerRunner is a new algorithm that visually clusters aptamers from next-generation sequencing data. This tool helps researchers identify promising aptamer candidates for experimental validation.
Area of Science:
- Biotechnology
- Bioinformatics
- Molecular Biology
Background:
- Aptamers are specific DNA/RNA ligands selected via Systematic Evolution of Ligands by EXponential enrichment (SELEX).
- Next-generation sequencing (NGS) enhances aptamer discovery but presents challenges in analyzing large datasets.
- Identifying high-likelihood candidate aptamers from NGS data requires advanced analytical tools.
Approach:
- AptamerRunner is a novel clustering algorithm for aptamer selection datasets.
- It generates visual networks of aptamers based on sequence and structural similarities.
- The algorithm integrates ranking data (e.g., fold enrichment) onto these networks.
Key Points:
- AptamerRunner facilitates the identification of diverse aptamer candidates.
- Visualizing related aptamers in a network provides context for selection.
- The tool aids in choosing aptamers with higher probabilities of successful experimental validation.
Conclusions:
- AptamerRunner offers a significant advancement over existing aptamer clustering methods.
- Its user-friendly design and visual data integration streamline aptamer discovery.
- The algorithm has broad implications for aptamer research across various scientific disciplines.
