A target enrichment approach for enhanced recovery of Synchytrium endobioticum nuclear genome sequences
Bibliographic record
Abstract
Potato wart disease is caused by the obligate fungal pathogen Synchytrium endobioticum. DNA extraction from compost, purified spores and crude wart tissue derived from tuber galls of infected potatoes often results in low S. endobioticum DNA concentration or highly contaminated with DNA coming from other microorganisms and the potato host. Therefore, Illumina sequencing of these samples generally results in suboptimal recovery of the nuclear genome sequences of S. endobioticum. A hybridization-based target enrichment protocol was developed to strongly enhance the recovery of S. endobioticum DNA while off-target organisms DNA remains uncaptured. The design strategy involved creating a set of 180,000 molecular baits targeting both gene and non-gene regions of S. endobioticum. The baits were applied to whole genome amplified DNA samples of various S. endobioticum pathotypes (races) in compost, from purified spores and crude wart tissue samples. This was followed by Illumina sequencing and bioinformatic analyses. Compared to non-enriched samples, target enriched samples: 1) showed a significant increase in the proportion of sequenced bases mapped to the S. endobioticum nuclear genome, especially for crude wart tissue samples; 2) yielded sequencing data with higher and better nuclear genome coverage; 3) biased genome assembly towards S. endobioticum sequences, yielding smaller assembly sizes but higher representation of putative S. endobioticum contigs; 4) showed an increase in the number of S. endobioticum genes detected in the genome assemblies. Our hybridization-based target enrichment protocol offers a valuable tool for enhancing genome sequencing and NGS-based molecular detection of S. endobioticum, especially in difficult samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".