De novo sequence assembly and characterization of the floral transcriptome in cross- and self-fertilizing plants
Bibliographic record
Abstract
BACKGROUND: The shift from cross-fertilization to predominant self-fertilization is among the most common evolutionary transitions in the reproductive biology of flowering plants. Increased inbreeding has important consequences for floral morphology, population genetic structure and genome evolution. The transition to selfing is usually characterized by a marked reduction in flower size and the loss of traits involved in pollinator attraction and the avoidance of self-fertilization. Here, we use short-read sequencing to assemble, de novo, the floral transcriptomes of three genotypes of Eichhornia paniculata, including an outcrosser and two genotypes from independently derived selfers, and a single genotype of the sister species E. paradoxa. By sequencing mRNA from tissues sampled at various stages of flower development, our goal was to sequence and assemble the floral transcriptome and identify differential patterns of gene expression. RESULTS: Our 24 Mbp assembly resulted in ~27,000 contigs that averaged ~900 bp in length. All four genotypes had highly correlated gene expression, but the three E. paniculata genotypes were more correlated with one another than each was to E. paradoxa. Our analysis identified 269 genes associated with floral development, 22 of which were differentially expressed in selfing lineages relative to the outcrosser. Many of the differentially expressed genes affect floral traits commonly altered in selfing plants and these represent a set of potential candidate genes for investigating the evolution of the selfing syndrome. CONCLUSIONS: Our study is among the first to demonstrate the use of Illumina short read sequencing for de novo transcriptome assembly in non-model species, and the first to implement this technology for comparing floral transcriptomes in outcrossing and selfing plants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".