Generation of a wheat leaf rust, <i>Puccinia triticina</i> , EST database from stage‐specific cDNA libraries
Bibliographic record
Abstract
SUMMARY: Thirteen cDNA libraries constructed from small amounts of leaf rust mRNA using optimized methods served as the source for the generation of 25 558 high-quality DNA sequence reads. Five life-cycle stages were sampled: resting urediniospores, urediniospores germinated over water or plant extract, compatible, interactive stages during appressorium or haustorium formation just before sporulation, and an incompatible interaction. mRNA populations were subjected to treatments such as full-length cDNA production, subtractive and normalizing hybridizations, and size selection methods combined with PCR amplification. Pathogen and host sequences from interactive libraries were differentiated in silico using cereal and fungal sequences, codon usage analyses, and by means of a partial prototype cDNA microarray hybridized with genomic DNAs. This yielded a non-redundant unigene set of 9760 putative fungal sequences consisting of 6616 singlets and 3144 contigs, representing 4.7 Mbp. At an E-value 10(-5), 3670 unigenes (38%) matched sequences in various databases and collections but only 694 unigenes (7%) were similar to genes with known functions. In total, 296 unigenes were identified as most probably wheat and ten as rRNA sequences. Annotation rates were low for germinated urediniospores (4%) and appressoria (2%). Gene sets obtained from the various life-cycle stages appear to be remarkably different, suggesting drastic reprogramming of the transcriptome during these major differentiation processes. Redundancy within contigs yielded information about possible expression levels of certain genes among stages. Many sequences were similar to genes from other rusts such as Uromyces and Melampsora species; some of these genes have been implicated in pathogenicity and virulence.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".