Development and Application of a Salmonid EST Database and cDNA Microarray: Data Mining and Interspecific Hybridization Characteristics
Bibliographic record
Abstract
We report 80,388 ESTs from 23 Atlantic salmon (Salmo salar) cDNA libraries (61,819 ESTs), 6 rainbow trout (Oncorhynchus mykiss) cDNA libraries (14,544 ESTs), 2 chinook salmon (Oncorhynchus tshawytscha) cDNA libraries (1317 ESTs), 2 sockeye salmon (Oncorhynchus nerka) cDNA libraries (1243 ESTs), and 2 lake whitefish (Coregonus clupeaformis) cDNA libraries (1465 ESTs). The majority of these are 3' sequences, allowing discrimination between paralogs arising from a recent genome duplication in the salmonid lineage. Sequence assembly reveals 28,710 different S. salar, 8981 O. mykiss, 1085 O. tshawytscha, 520 O. nerka, and 1176 C. clupeaformis putative transcripts. We annotate the submitted portion of our EST database by molecular function. Higher- and lower-molecular-weight fractions of libraries are shown to contain distinct gene sets, and higher rates of gene discovery are associated with higher-molecular weight libraries. Pyloric caecum library group annotations indicate this organ may function in redox control and as a barrier against systemic uptake of xenobiotics. A microarray is described, containing 7356 salmonid elements representing 3557 different cDNAs. Analyses of cross-species hybridizations to this cDNA microarray indicate that this resource may be used for studies involving all salmonids.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.003 |
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".