Expressed sequence tag analysis of zebrafish eye tissues for NEIBank.
Bibliographic record
Abstract
PURPOSE: To characterize gene expression patterns in various tissues of the zebrafish (Danio rerio) eye and identify zebrafish orthologs of human genes by expressed sequence tag (EST) analysis for NEIBank. METHODS: mRNA was extracted from adult zebrafish eye tissues, including lenses, anterior segments (minus lens), retinas, posterior segments lacking retinas, and whole eyes. Five different cDNA libraries were constructed in the pCMVSport6 vector. Approximately 4,000 clones from each library were sequenced and analyzed using various bioinformatics programs. RESULTS: The analysis yielded approximately 2,500 different gene clusters for each library. Combining data from the five libraries produced 10,392 unique gene clusters. GenBank accession numbers were identified for 37.6% (3,906) of the total gene clusters in the combined libraries and approximately 50% were linked to Unigene clusters in the current database. Several new crystallin genes, including two gammaN-crystallins, and a second major intrinsic protein (MIP) were identified in the lens library. In addition, a zebrafish homolog of cochlin (COCH), a gene that may play a role in the pathogenesis of human glaucoma, was identified in the anterior segment library. Surprisingly, no clear ortholog of the major retinal transcription factor Nrl was identified. CONCLUSIONS: The zebrafish eye tissue cDNA libraries are a useful resource for comparative gene expression analysis. These libraries will complement the cDNA libraries made for the Zebrafish Gene Collection (ZGC) and provide an additional source for gene identification and characterization in the vertebrate eye.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".