Transcriptome, proteome and draft genome of Euglena gracilis
Bibliographic record
Abstract
BACKGROUND: Photosynthetic euglenids are major contributors to fresh water ecosystems. Euglena gracilis in particular has noted metabolic flexibility, reflected by an ability to thrive in a range of harsh environments. E. gracilis has been a popular model organism and of considerable biotechnological interest, but the absence of a gene catalogue has hampered both basic research and translational efforts. RESULTS: We report a detailed transcriptome and partial genome for E. gracilis Z1. The nuclear genome is estimated to be around 500 Mb in size, and the transcriptome encodes over 36,000 proteins and the genome possesses less than 1% coding sequence. Annotation of coding sequences indicates a highly sophisticated endomembrane system, RNA processing mechanisms and nuclear genome contributions from several photosynthetic lineages. Multiple gene families, including likely signal transduction components, have been massively expanded. Alterations in protein abundance are controlled post-transcriptionally between light and dark conditions, surprisingly similar to trypanosomatids. CONCLUSIONS: Our data provide evidence that a range of photosynthetic eukaryotes contributed to the Euglena nuclear genome, evidence in support of the 'shopping bag' hypothesis for plastid acquisition. We also suggest that euglenids possess unique regulatory mechanisms for achieving extreme adaptability, through mechanisms of paralog expansion and gene acquisition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".