Generation of a large scale repertoire of Expressed Sequence Tags (ESTs) from normalised rainbow trout cDNA libraries
Bibliographic record
Abstract
BACKGROUND: Within the framework of a genomics project on livestock species (AGENAE), we initiated a high-throughput DNA sequencing program of Expressed Sequence Tags (ESTs) in rainbow trout, Oncorhynchus mykiss. RESULTS: We constructed three cDNA libraries including one highly complex pooled-tissue library. These libraries were normalized and subtracted to reduce clone redundancy. ESTs sequences were produced, and 96,472 ESTs corresponding to high quality sequence reads were released on the international database, currently representing 42.5% of the overall sequence knowledge in this species. All these EST sequences and other publicly available ESTs in rainbow trout have been included on a publicly available Website (SIGENAE) and have been clustered into a total of 52,930 clusters of putative transcripts groups, including 24,616 singletons. 57.1% of these 52,930 clusters are represented by at least one Agenae EST and 14,343 clusters (27.1%) are only composed by Agenae ESTs. Sequence analysis also reveals that normalization and especially subtraction were effective in decreasing redundancy, and that the pooled-tissue library was representative of the initial tissue complexity. CONCLUSION: Due to present work on the construction of rainbow trout normalized cDNA libraries and their extensive sequencing, along with other large scale sequencing programs, rainbow trout is now one of the major fish models in term of EST sequences available in a public database, just after Zebrafish, Danio rerio. This information is now used for the selection of a non redundant set of clones for producing DNA micro-arrays in order to examine global gene expression.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".