AptaTRACE：通过揭示HT-SELEX实验中的选择趋势来阐明序列-结构结合基序
AptaTRACE: Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments
摘要：
适体是合成的短RNA/DNA分子，以高亲和力和特异性结合特异性靶标，在越来越多的生物医学应用中被利用。通过指数富集配体系统进化（SELEX）方案在体外鉴定适体。SELEX通过一个迭代过程选择结合剂，该过程从随机ssDNA/RNA序列池开始，通过一系列选择循环扩增靶-仿射物质。HT-SELEX将SELEX与高通量测序相结合，最近改变了适体的开发，并为更多的应用开辟了领域。HT-SELEX能够生成超过5亿个数据点，挑战计算科学家识别适体属性的任务，例如确定结合的序列结构基序。虽然目前可用的基序发现方法建议部分解决这个问题，没有一个具有HT-SELEX数据所需的通用性或可扩展性，并且它们没有利用实验过程的重要特性。我们提出了AptaTRACE，一种新的方法用于鉴定HT-SELEX衍生的适体中的序列结构结合基序。我们的方法利用了SELEX协议的实验设计，并确定了显示选择签名的序列结构基序。由于其独特的方法，AptaTRACE可以发现基序，即使这些基序只存在于池的一小部分。由于这些特征，我们的方法可以帮助减少产生具有所需特性的适体所需的选择循环的数量，从而减少这种相当昂贵的程序的成本和时间。模拟和真实数据的方法的性能表明，AptaTRACE可以检测序列结构基序，即使在高度挑战性的数据。
英文摘要：
Aptamers, short synthetic RNA/DNA molecules binding specific targets with high affinity and specificity, are utilized in an increasing spectrum of bio-medical applications. Aptamers are identified in vitro via the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) protocol. SELEX selects binders through an iterative process that, starting from a pool of random ssDNA/RNA sequences, amplifies target-affine species through a series of selection cycles. HT-SELEX, which combines SELEX with high throughput sequencing, has recently transformed aptamer development and has opened the field to even more applications. HT-SELEX is capable of generating over half a billion data points, challenging computational scientists with the task of identifying aptamer properties such as sequence structure motifs that determine binding. While currently available motif finding approaches suggest partial solutions to this question, none possess the generality or scalability required for HT-SELEX data, and they do not take advantage of important properties of the experimental procedure. We present AptaTRACE, a novel approach for the identification of sequence-structure binding motifs in HT-SELEX derived aptamers. Our approach leverages the experimental design of the SELEX protocol and identifies sequence-structure motifs that show a signature of selection. Because of its unique approach, AptaTRACE can uncover motifs even when these are present in only a minuscule fraction of the pool. Due to these features, our method can help to reduce the number of selection cycles required to produce aptamers with the desired properties, thus reducing cost and time of this rather expensive procedure. The performance of the method on simulated and real data indicates that AptaTRACE can detect sequence-structure motifs even in highly challenging data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
