Aptamers are short single-stranded DNA or RNA molecules that fold into 3D shapes capable of binding specific targets (proteins, small molecules, cells) with high affinity and selectivity. The classic way to discover them is SELEX(Systematic Evolution of Ligands by EXponential enrichment): iterative rounds of binding, partitioning, amplification, and re-selection. What changed the field is high-throughput sequencing (HT-SELEX)—sequencing pools after each round—turning SELEX into a data-rich optimization problem where bioinformatics is no longer optional but central to identifying true binders, understanding enrichment dynamics, and avoiding artifacts. This article explains how bioinformatics for aptamer selection works end-to-end, what signals to extract from sequencing data, how to connect sequence to structure and function, and where modern machine learning fits—without relying on external case studies or outbound links. 1) Why Bioinformatics Matters in Aptamer Selection Traditional SELEX often ends with testing a handful of sequences from late rounds. HT-SELEX changes the game by giving you: Population-level visibility: you can track millions of sequences across rounds, not just a few clones. Early discovery: promising families can emerge before the pool looks “clean,” enabling earlier decision-making and fewer wet-lab rounds when combined with modeling. Artifact detection: PCR bias, sequencing errors, and “sticky” motifs can…
