Long-read reconstruction of many diverse haplotypes with devider
Bibliographic record
Abstract
Reconstructing exact haplotypes is important when sequencing a mixture of similar sequences. Long-read sequencing can connect distant alleles to disentangle similar haplotypes, but handling sequencing errors requires specialized techniques. Here, we present devider , an algorithm for haplotyping small sequences, such as viruses or genes, from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. On a synthetic Oxford Nanopore Technologies (ONT) long-read data set containing seven HIV strains, devider recovers 97% of the haplotype content and has the most accurate abundance estimates while taking <4 min and 1 GB of memory for >8000× coverage. Benchmarking on synthetic mixtures of antimicrobial-resistance (AMR) genes shows that devider recovers 83% of haplotypes, 23 percentage points higher than the next best method. On real Pacific Biosciences (PacBio) and ONT data sets, devider recapitulates previously known results in seconds, disentangling a bacterial community with more than 10 strains and an HIV-1 coinfection data set. We use devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering 13 distinct haplotypes for a tet(Q) tetracycline-resistance gene with >18,000× coverage and six haplotypes for a CfxA2 beta-lactamase gene. We find clear recombination blocks for these AMR gene haplotypes, showcasing devider's ability to unveil evolutionary signals for heterogeneous mixtures.
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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".