ArticleCell systems2016
AptaTRACE Elucidates RNA Sequence-Structure Motifs from Selection Trends in HT-SELEX Experiments.
Article in Cell systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Review
- Are we there yet with XNA aptamers?RSC advances · 2026Review
- In silico aptamer design: from sequence selection to structural optimization and computational modelling strategies.Journal of computer-aided molecular design · 2026Review
- A Data-Analysis Pipeline for High-Throughput Systematic Evolution of Ligands by Exponential Enrichment (HT-SELEX) in the Characterization of Telomeric Proteins.bioRxiv : the preprint server for biology · 2026Article
- GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability.Mathematical biosciences · 2025Article
- DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.Molecular therapy. Nucleic acids · 2025Article
- AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers.Molecular therapy. Nucleic acids · 2024Article
- In vitro selection of DNA aptamers against staphylococcal enterotoxin A.Scientific reports · 2024Article
- Aptamers as an approach to targeted cancer therapy.Cancer cell international · 2024Review
- AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers.bioRxiv : the preprint server for biology · 2023Article
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
- DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.PLoS computational biology · 2023Article
- Design and Prediction of Aptamers Assisted by In Silico Methods.Biomedicines · 2023Review
- Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?Nano today · 2022Article
- FASTAptameR 2.0: A web tool for combinatorial sequence selections.Molecular therapy. Nucleic acids · 2022Article
- Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning.Nature communications · 2022Article
- Selection and Characterization of Vimentin-Binding Aptamer Motifs for Ovarian Cancer.Molecules (Basel, Switzerland) · 2021Article
- In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.Molecular diversity · 2021Review
- Machine learning guided aptamer refinement and discovery.Nature communications · 2021Article
- Artificial Intelligence in Aptamer-Target Binding Prediction.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Aptamers, short RNA or DNA molecules that bind distinct targets with high affinity and specificity, can be identified using high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX), but scalable analytic tools for understanding sequence-function relationships from diverse HT-SELEX data are not available. Here we present AptaTRACE, a computational approach that leverages the experimental design of the HT-SELEX protocol, RNA secondary structure, and the potential presence of many secondary motifs to identify sequence-structure motifs that show a signature of selection. We apply AptaTRACE to identify nine motifs in C-C chemokine receptor type 7 targeted by aptamers in an in vitro cell-SELEX experiment. We experimentally validate two aptamers whose binding required both sequence and structural features. AptaTRACE can identify low-abundance motifs, and we show through simulations that, because of this, it could lower HT-SELEX cost and time by reducing the number of selection cycles required.
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