RNA sequencing describes both population structure and plasticity-selection dynamics in a non-model fish
Bibliographic record
Abstract
BACKGROUND: Messenger RNA sequencing is becoming more common in studies of non-model species and is most often used for gene expression-based investigations. However, the method holds potential for numerous other applications as well-including analyses of alternative splicing, population structure, and signatures of selection. To maximize the utility of mRNA data sets, distinct analyses may be combined such as by exploring dynamics between gene expression with signatures of selection in the context of population structure. Here, we compare two published data sets describing two populations of a minnow species endemic to the San Francisco Estuary (Sacramento splittail, Pogonichthys macrolepidotus): a microsatellite data set showing population structure, and an mRNA whole transcriptome data set obtained after the two populations were exposed to a salinity challenge. We compared measures of population structure and genetic variation using single nucleotide polymorphisms (SNPs) called from mRNA from the whole transcriptome sequencing study with those patterns determined from microsatellites. For investigating plasticity and evolution, intra- and inter-population transcriptome plasticity was investigated with differential gene expression, differential exon usage, and gene expression variation. Outlier SNP analysis was also performed on the mRNA data set and signatures of selection and phenotypic plasticity were investigated on an individual-gene basis. RESULTS: We found that mRNA sequencing revealed patterns of population structure consistent with those found with microsatellites, but with lower magnitudes of genetic variation and population differentiation consistent with widespread purifying selection expected when using mRNA. In addition, within individual genes, phenotypic plasticity or signatures of selection were found in almost mutual exclusion (except heatr6, nfu1, slc22a6, sya, and mmp13). CONCLUSIONS: These results show that an mRNA sequencing data set may have multiple uses, including describing population structure and for investigating the mechanistic interplay of evolution and plasticity in adaptation. MRNA sequencing thus complements traditional sequencing methods used for population genetics, in addition to its utility for describing phenotypic plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".