The Next Generation Is Here: A Review of Transcriptomic Approaches in Marine Ecology
Bibliographic record
Abstract
As we enter the United Nations Decade of Ocean Science for Sustainable Development, extensive efforts to reverse the decline of ocean health are taking place. Moreover, the need to utilize innovative and integrative approaches to aid in these efforts and address marine ecological questions are urgent. Transcriptomic technologies provide tools to further our understanding of an organism’s biology by allowing researchers to rapidly gain information on the genetic variation of populations and the regulation of cellular processes and pathways through gene presence, absence, and expression. Here, we review the application of transcriptomics in the field of marine ecology over the last decade, following a systematic literature review approach. We found 478 articles that fit our search criteria of using transcriptomic approaches to address ecological hypotheses, with 70% of these studies occurring within the last 5 years. Among the analysed articles, 51.7% involved a type of stressor, 16.6% used transcriptomics to study adaptation, and another 15.9% researched ecological interactions. Most articles investigated species from kingdom Animalia, with a high representation from both molluscs (19.5%) and chordates (13.3%), and only 22% of studies had a fieldwork component. Our review demonstrates how the use of transcriptomic techniques in the field of marine ecology is increasing and how they are being applied. Although there are still challenges researchers experience using such techniques, particularly when annotating genes in non-model species and those with no prior genomic resources, these innovative technologies are extremely valuable in investigating differential gene expression, molecular pathways, and generating genomic resources.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".