Distinct sequencing success at non-B-DNA motifs
Bibliographic record
Abstract
Abstract Modern sequencing technologies are not error-free, and might have elevated error rates at some locations of the genome. A potential cause for such elevated error rates is the formation of alternative DNA structures (non-B DNA), such as G-quadruplexes (G4s), Z-DNA, or cruciform structures, during sequencing. Approximately 13% of the human genome has the potential to form such structures, which have been previously shown to affect the activity of DNA polymerases and helicases. Here we tested whether motifs with the potential to form non-B DNA (non-B motifs) influence the sequencing success of three major sequencing technologies—Illumina, Pacific Biosciences (PacBio) HiFi, and Oxford Nanopore Technologies (ONT). We estimated sequencing success by computing the rates of single-nucleotide, insertion, and deletion errors, as well as by evaluating mean read depth and mean base quality. Overall, all technologies exhibited altered sequencing success for most non-B motif types. Single-nucleotide error rates were generally increased for G-quadruplexes (G4s) and Z-DNA motifs in all three technologies. Illumina and PacBio HiFi deletion error rates were also increased for all non-B types except for Z-DNA motifs, while in ONT they were increased substantially only for G4 motifs. Insertion error rates for non-B motifs were highly elevated in Illumina, moderately elevated in PacBio HiFi, and only slightly elevated in ONT. Using Poisson regression modeling, we evaluated how non-B DNA motifs and other factors influence sequencing error profiles. Using the error rates at non-B motifs, we developed a probabilistic approach to determine the number of false-positive single-nucleotide variants (SNVs) in different sample size and variant frequency cutoff scenarios, as well as in previously generated sequencing data sets (1000Genomes, Simons Genome Diversity Project, and gnomAD). Overall, the effect of non-B DNA on sequencing should be considered in downstream analyses, particularly in studies with limited read depth—e.g., single-cell and ancient DNA sequencing, as well as sequencing of pooled population samples—and when scoring variants with low frequency (e.g., singletons). Because each sequencing technology analyzed has a unique error profile at non-B motifs, a combination of different technologies should be considered in future sequencing studies of such motifs, to maximize accuracy.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".