Ecological proteomics: Finding molecular markers that matter
Bibliographic record
Abstract
It is becoming increasingly clear that local adaptation can occur even in the face of high gene flow and limited overall genomic differentiation among populations (reviewed by Nosil et al. 2009). Thus, one important task for molecular ecologists is to sift through genomic data to identify the genes that matter for local adaptation (Hoffmann & Willi 2008; Stapley et al. 2010). Recent advances in high-throughput molecular technologies have facilitated this search, and a variety of approaches can be applied, including those grounded in population genetics [e.g. outlier analysis (Pavlidis et al. 2008)], classical and quantitative genetics [e.g. quantitative trait locus analysis (MacKay et al. 2009)], and cellular and molecular biology [e.g. transcriptomics (Larsen et al. 2011)]. However, applying these approaches in nonmodel organisms that lack extensive genetic and genomic resources has been a formidable challenge. In this issue, Papakostas et al. (2012). demonstrate how one such approach – high-throughput label-free proteomics (reviewed by Gstaiger & Aebersold 2009; Domon & Aebersold 2010) – can be applied to detect genes that may be involved in local adaptation in a species with limited genomic resources. Using this approach, they identified genes that may be implicated in local adaptation to salinity in European whitefish (Coregonus lavaretus L.) and provide insight into the mechanisms by which fish cope with changes in this critically important environmental parameter.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".