Development and evaluation of a target enrichment bait set for phylogenetic analysis of oomycetes
Bibliographic record
Abstract
Target enrichment is a term that encompasses multiple related approaches where desired genomic regions are captured by molecular baits, leaving behind redundant or non-target regions in the genome, followed by amplification and next-generation sequencing of those captured regions. A molecular bait set was developed based on 426 single-copy, oomycete-specific orthologs and 3 barcoding genes. The bait set was tested on 27 oomycete samples (belonging to the Saprolegniales, Albuginales, and Peronosporales) derived from live and herbarium specimens, as well as control samples of true fungi and plants. Results show that (i) our method greatly enriches for the targeted orthologs on oomycete samples, but insignificantly on fungal and plant samples; (ii) an average of 263 out of 429 orthologs (61%) were recovered from oomycete live and herbarium specimens; (iii) sequencing roughly 100 000 read pairs per sample is sufficient for optimal ortholog recovery while maintaining low sequencing costs; and (iv) the expected relationships were recovered by phylogenetic analysis from the data generated. This is the first report of an oomycete-specific target enrichment method with broad potential applications for evolutionary and taxonomic studies. A key benefit of our target enrichment method is that it allows researchers to easily unlock the vast and unexplored oomycete genomic diversity stored in natural history collections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".