Identification, Characterization, and Mapping of Expressed Sequence Tags from an Embryonic Zebrafish Heart cDNA Library
Bibliographic record
Abstract
The generation of expressed sequence tags (ESTs) has proven to be a rapid and economical approach by which to identify and characterize expressed genes. We generated 5102 ESTs from a 3-d-old embryonic zebrafish heart cDNA library. Of these, 57.6% matched to known genes, 14.2% matched only to other ESTs, and 27.8% showed no match to any ESTs or known genes. Clustering of all ESTs identified 359 unique clusters comprising 1771 ESTs, whereas the remaining 3331 ESTs did not cluster. This estimates the number of unique genes identified in the data set to be approximately 3690. A total of 1242 unique known genes were used to analyze the gene expression patterns in the zebrafish embryonic heart. These were categorized into seven categories on the basis of gene function. The largest class of genes represented those involved in gene/protein expression (25.9% of known transcripts). This class was followed by genes involved in metabolism (18.7%), cell structure/motility (16.4%), cell signaling and communication (9.6%), cell/organism defense (7.1%), and cell division (4.4%). Unclassified genes constituted the remaining 17.91%. Radiation hybrid mapping was performed for 102 ESTs and comparison of map positions between zebrafish and human identified new synteny groups. Continued comparative analysis will be useful in defining the boundaries of conserved chromosome segments between zebrafish and humans, which will facilitate the transfer of genetic information between the two organisms and improve our understanding of vertebrate evolution.
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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) | 0.001 | 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".