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Record W2902333034 · doi:10.2144/btn-2018-0123

Oxford Nanopore Sequencing Enables Rapid Discovery of Single-Domain Antibodies from Phage Display Libraries

2018· article· en· W2902333034 on OpenAlexaff
Michael J. Lowden, Kevin A. Henry

Bibliographic record

VenueBioTechniques · 2018
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPanning (audio)MinionNanopore sequencingPhage displayComputational biologyBiologySingle-domain antibodyGenomic libraryMassive parallel sequencingDNA sequencingGeneticsAntibody RepertoireAntibodyGeneBase sequence

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.295
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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