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Record W4286685640 · doi:10.1101/2022.07.22.501121

An assessment of Nano-RECall: Interpretation of Oxford Nanopore sequence data for HIV-1 drug resistance testing

2022· preprint· en· W4286685640 on OpenAlexaff
Kayla Delaney, Trevor Ngobeni, Conan K. Woods, Carli Gordijn, Mathilda Claassen, Urvi M. Parikh, P. Richard Harrigan, Gert U. van Zyl

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilNational Health Laboratory ServiceNational Institutes of HealthUniversiteit StellenboschSouth African Medical Research Council
KeywordsSanger sequencingNanopore sequencingAmpliconMinionComputational biologyBiologyConsensus sequenceGenotypingHIV drug resistanceGeneticsDrug resistanceDNA sequencingHuman immunodeficiency virus (HIV)Polymerase chain reactionGeneGenotypeVirologyPeptide sequenceViral load

Abstract

fetched live from OpenAlex

Abstract Introduction Oxford Nanopore Technologies (ONT) offer sequencing with low-capital-layout sequencing options, which could assist in expanding HIV drug resistance testing to resource limited settings. However, sequence analysis remains time time-consuming and reliant on skilled personnel. Moreover, current ONT bioinformatic pipelines provide a single consensus sequence that is not equivalent to Sanger sequencing, as drug resistance is often detected in mixed populations. We have therefore investigated an integrated bioinformatic pipeline, Nano-RECall, for seamless drug resistance of low read coverage ONT sequence data from affordable Flongle or MinION flow cells. Methods We compared Sanger sequencing to ONT sequencing of the same HIV-1 subtype C polymerase chain reaction (PCR) amplicons, respectively using RECall and the novel Nano-RECall bioinformatics pipelines. Amplicons were from separate assays a) Applied Biosystems HIV-1 Genotyping Kit (ThermoFisher) spanning protease (PR) to reverse transcriptase (RT) (PR-RT) (n=46) and b) homebrew integrase (IN) (n=21). We investigated optimal read-depth by assessing the coefficient of variation (CV) of nucleotide proportions for various read-depths; and between replicates of 400 reads. The agreement between Sanger sequences and ONT sequences were assessed at nucleotide level, and at codon level for Stanford HIV drug resistance database mutations. Results The coefficient of variation of ONT minority variants plateaued after a read depth of 400-fold implying limited benefit of additional depth and replicates of 400 reads showed a CV of ∼6 % for a representative position. The average sequence similarity between ONT and Sanger sequences was 99.3% (95% CI: 99.1-99.4%) for PR-RT and 99.6% (95% CI: 99.4-99.7%) for INT. Drug resistance mutations did not differ for 21 IN sequences; 16 mutations were detected by both ONT- and Sanger sequencing. For the 46 PR and RT sequences, 245 mutations were detected by either ONT or Sanger, of these 238 (97.1%) were detected by both. Conclusions The Nano-RECall pipeline, freely available as a downloadable application on a Windows computer, provides Sanger-equivalent HIV drug resistance interpretation. This novel pipeline combined with a simple workflow and multiplexing samples on ONT flow-cells would contribute to making HIV drug resistance sequencing feasible for resource limited settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.318
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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