Applications for next-generation sequencing in fish ecotoxicogenomics
Bibliographic record
Abstract
The new technologies for next-generation sequencing (NGS) and global gene expression analyses that are widely used in molecular medicine are increasingly applied to the field of fish biology. This has facilitated new directions to address research areas that could not be previously considered due to the lack of molecular information for ecologically relevant species. Over the past decade, the cost of NGS has decreased significantly, making it possible to use non-model fish species to investigate emerging environmental issues. NGS technologies have permitted researchers to obtain large amounts of raw data in short periods of time. There have also been significant improvements in bioinformatics to assemble the sequences and annotate the genes, thus facilitating the management of these large datasets.The combination of DNA sequencing and bioinformatics has improved our abilities to design custom microarrays and study the genome and transcriptome of a wide variety of organisms. Despite the promising results obtained using these techniques in fish studies, NGS technologies are currently underused in ecotoxicogenomics and few studies have employed these methods. These issues should be addressed in order to exploit the full potential of NGS in ecotoxicological studies and expand our understanding of the biology of non-model organisms.
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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 | 0.006 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".