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 towards a preferred structure. In the initial pool, secondary structural contexts of each k-mer are distributed according to a background distribution. However, for sequence motifs involved in binding, in later selection cycles, this distribution becomes biased towards the structural context favored by the binding interaction with the target site. Thus, AptaTRACE aims at identifying sequence motifs whose tendency of residing in a hairpin, bugle loop, inner loop, multiple loop, dangling end, or of being paired converges to a specific structural context throughout the selection cycles of HT-SELEX experiments. For each k-mer, we compute the distribution of its structural contexts in each sequenced pool. Then, we compute the relative entropy (KL-divergence) based score, to capture the change in the distribution of its secondary structure contexts from a cycle to a later cycle. The relative entropy based score is thus an estimate of the selection towards the preferred secondary structure(s). We show our results of applying AptaTRACE to simulated data and an in vitro selection consisting of high-throughput data from 9 rounds of cell-SELEX. In testing on simulated data, AptaTRACE outperformed other generic motif finding methods in terms of sensitivity. By measuring selection towards sequence-structure motifs by the change in their distributions of the structural contexts and not based on abundance, AptaTRACE can uncover motifs even when these are present only in a small fraction of the pool. Moreover, our method can also 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.
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