Oxford Nanopore Sequencing Enables Rapid Discovery of Single-Domain Antibodies from Phage Display Libraries
Bibliographic record
Abstract
Antibody (Ab) repertoire sequencing using high-throughput massively parallel technologies has contributed substantially to the understanding of Ab responses following infection, vaccination and autoimmunity. Because individual B-cell receptors are recombined and diversified somatically, genomic comparisons are limited, and distinguishing rare variants from sequencing errors is a major challenge. Oxford Nanopore Technologies' MinION is a highly portable and cost-effective third-generation sequencing instrument, but has not been used for Ab repertoire sequencing due to its high error rate (approximately 1/10 bases). Here, we applied nanopore sequencing to single-domain Ab (sdAb) repertoires and phage-displayed sdAb libraries. We show that despite low overall data fidelity, sdAb sequences could be reconstructed above a frequency threshold (∼100 copies); however, distinguishing clonal sdAb variants was not always possible. The data quality was sufficient to enable rapid identification of antigen-specific sdAb sequences enriched during panning of phage display libraries, obviating the need for screening single clones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".