Quantitative Comparison of Capture-SELEX, GO-SELEX, and Gold-SELEX for Enrichment of Aptamers
Bibliographic record
Abstract
Since 1990, numerous methods for aptamer selection have been developed, although a quantitative comparison of their sequence enrichment is lacking. In this study, we compared the enrichment factors of three library-immobilization SELEX methods (capture-SELEX, GO-SELEX, and gold-SELEX). We used a spiked library that contained multiple DNA aptamers with different affinities for adenosine. The aptamer separation efficiency was measured using qPCR, and all of the three methods showed a very low DNA release (<1%) in the presence of 100 μM adenosine. Among these, barely any DNA was released from the gold nanoparticles. Deep sequencing was used to compare the enrichment of three aptamers: Ade1301, Ade1304, and the classical aptamer. Enrichment up to 30 to 50-fold was observed only for the capture-SELEX method, whereas the other two methods showed enrichment factors below 1. By blocking the primer-binding regions of the library, GO-SELEX reached up to 14% enrichment. Finally, the enrichment of aptamers based on nonspecific release and target-induced release was discussed, and the advantages of capture-SELEX were rationalized. Taken together, these results indicate that capture-SELEX is a much more efficient method for enriching aptamers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".