Automating HIV Drug Resistance Genotyping with RECall, a Freely Accessible Sequence Analysis Tool
Bibliographic record
Abstract
Genotypic HIV drug resistance testing is routinely used to guide clinical decisions. While genotyping methods can be standardized, a slow, labor-intensive, and subjective manual sequence interpretation step is required. We therefore performed external validation of our custom software RECall, a fully automated sequence analysis pipeline. HIV-1 drug resistance genotyping was performed on 981 clinical samples at the Stanford Diagnostic Virology Laboratory. Sequencing trace files were first interpreted manually by a laboratory technician and subsequently reanalyzed by RECall, without intervention. The relative performances of the two methods were assessed by determination of the concordance of nucleotide base calls, identification of key resistance-associated substitutions, and HIV drug resistance susceptibility scoring by the Stanford Sierra algorithm. RECall is freely available at http://pssm.cfenet.ubc.ca. In total, 875 of 981 sequences were analyzed by both human and RECall interpretation. RECall analysis required minimal hands-on time and resulted in a 25-fold improvement in processing speed (∼150 technician-hours versus ∼6 computation-hours). Excellent concordance was obtained between human and automated RECall interpretation (99.7% agreement for >1,000,000 bases compared). Nearly all discordances (99.4%) were due to nucleotide mixtures being called by one method but not the other. Similarly, 98.6% of key antiretroviral resistance-associated mutations observed were identified by both methods, resulting in 98.5% concordance of resistance susceptibility interpretations. This automated sequence analysis tool provides both standardization of analysis and a significant improvement in data workflow. The time-consuming, error-prone, and dreadfully boring manual sequence analysis step is replaced with a fully automated system without compromising the accuracy of reported HIV drug resistance data.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".