Structural variation discovery in wheat using PacBio high-fidelity sequencing
Bibliographic record
Abstract
Abstract Background Structural variations (SVs) pervade plant genomes and contribute substantially to the phenotypic diversity. However, most SVs were ineffectively assayed because of their complex nature and the limitations of early genomic technologies. The recent advance in third-generation sequencing, particularly the PacBio high-fidelity (HiFi) sequencing technology, produces highly accurate long-reads and offers an unprecedented opportunity to characterize SVs’ structure and functionality. As HiFi sequencing is relatively new to population genomics, it is imperative to evaluate and optimize HiFi sequencing based SV detection before applying the technology at scale. Results We sequenced wheat genomes using HiFi reads, followed by a comprehensive evaluation of mainstream long-read aligners and SV callers in SV detection. The results showed that the accuracy of deletion discovery is markedly influenced by callers, which account for 87.73% of the variance, while both aligners (38.25%) and callers (49.32%) contributed substantially to the accuracy variance for insertions. Among the aligners, Winnowmap2 and NGMLR excelled in detecting deletions and insertions, respectively. For SV callers, SVIM achieved the best performance. We demonstrated that combining the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the effect of sequencing depth on the accuracy of SV detection, showing that low-coverage HiFi sequencing is sufficiently robust for high-quality SV discovery. Conclusions This study thoroughly evaluated SV discovery approaches using HiFi reads, establishing optimal workflows to investigate structural variations in the wheat genome. The notable accuracy of SV discovery from low-coverage HiFi sequencing indicates that skim HiFi sequencing is effective and preferable to characterize SVs at the population level. This study will help advance SV discovery and decipher the biological functions of SVs in wheat and many other plants.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".