Ruiz-Ciancio Dario, Veeramani Suresh, Singh Rahul, Embree Eric, Ortman Chris, Thiel Kristina W, Thiel William H
Instituto de Ciencias Biomédicas (ICBM), Facultad de Ciencias Médicas, Universidad Católica de Cuyo, Av. José Ignacio de la Roza 1516, Rivadavia 5400, San Juan, Argentina.
National Council of Scientific and Technical Research (CONICET), Godoy Cruz 2290, C1425FQB Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina.
Mol Ther Nucleic Acids. 2024 Oct 9;35(4):102358. doi: 10.1016/j.omtn.2024.102358. eCollection 2024 Dec 10.
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.
适体是对靶标具有高亲和力和特异性的短单链DNA或RNA分子,通过指数富集配体的迭代系统进化(SELEX)过程产生。与桑格测序相比,下一代测序(NGS)通过允许对SELEX富集的适体进行更全面的分析,彻底改变了适体的筛选。目前适体NGS数据集面临的挑战是识别出最有可能成功进行实验验证的多样化候选适体群体。在此,我们展示了AptamerRunner,一种适体序列和/或结构聚类算法,它将计算分析与可视化以及专业指导的决策协同整合。将网络化的适体与排名数据(如富集倍数或评分算法结果)进行可视化整合,通过提供一个自然的背景来描绘适体组,从中可以选择排名或评分的候选适体进行实验验证,这代表了相对于现有聚类工具的重大进步。AptamerRunner固有的灵活性、用户友好的设计以及未来改进的前景,对广泛学科领域的适体研究人员具有广泛而深远的影响。