{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE336nnn/GSE336208/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Other"],"species":["synthetic construct"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE336208"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"From enrichment to inference: a multi-target framework for scalable aptamer discovery","description":"SELEX has greatly advanced aptamer discovery; however, traditional workflows face considerable challenges in scaling due to the labor-intensive screening and validation processes. More critically, in conventional SELEX, sequencing abundance does not always correlate directly with binding properties due to amplification bias, matrix adsorption, and initial copy-number advantages. We establish an automated framework that converts enrichment readouts into affinity- and specificity-informed candidate prioritization. The framework integrates two key innovations: a distribution-reset secondary screening and a relative abundance ratio for candidate selection. Furthermore, the 96-target workflow generates a systematically organized sequence-target dataset that can be utilized as a valuable resource for analyzing SELEX mechanisms and facilitating AI-assisted ap-tamer discovery. Here, we release the sequencing data of the enriched libraries. Within each sequence-protein matrix, the dataset integrates raw read counts, relative abundance ratios, sequence ranks, and target identities into a unified format. This structure preserves both successful and unsuccessful screening results, which are crucial for com-prehending target screenability and distinguishing genuine target-specific enrichment from general back-ground retention.","dates":{"publication":"2026/06/23"},"accession":"GSE336208","cross_references":{"GSM":["GSM9830041","GSM9830042","GSM9830043","GSM9830044","GSM9830045","GSM9830046","GSM9830039","GSM9830040"],"GPL":["37133","26526"],"GSE":["336208"],"taxon":["synthetic construct"]}}