AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative systematic evolution of ligands by exponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Here we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner have broad-reaching implications for aptamer researchers across a wide range of disciplines.

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Publication Details

Journal
Molecular Therapy — Nucleic Acids
Published
2024-10-09
DOI
https://doi.org/10.1016/j.omtn.2024.102358
Citations
9
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
2.10

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article

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Suresh Veeramani, William H. Thiel, Chris Ortman, Kristina W. Thiel et al.
9 citations
Molecular Therapy — Nucleic Acids
RNA and protein synthesis mechanisms
2.10
article

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Suresh Veeramani, William H. Thiel, Chris Ortman, Kristina W. Thiel, Dario Ruiz-Ciancio, Eric Embree, Rahul Singh
article en
9 citations

Abstract

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative systematic evolution of ligands by exponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Here we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner have broad-reaching implications for aptamer researchers across a wide range of disciplines.

Molecular Therapy — Nucleic AcidsVol. 35(4)
University of Iowa (US), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Vall d'Hebron Institut de Recerca (ES), Universidad Católica de Cuyo (AR)
National Science Foundation, U.S. Department of Defense, American Cancer Society, American Heart Association, National Institutes of Health, Holden Comprehensive Cancer Center, University of Iowa, National Cancer Institute, Congressionally Directed Medical Research Programs, NHLBI Division of Intramural Research, DOD Peer Reviewed Cancer Research Program
Openalex Percentile: Top 12%
RNA and protein synthesis mechanisms
2.10
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