Eukan: a fully automated nuclear genome annotation pipeline for less studied and divergent eukaryotes
Bibliographic record
Abstract
Abstract Here, we introduce a new annotation pipeline, called Eukan, designed to deliver reliably high-quality results across a broad range of eukaryotes. First, experimental evidence is automatically leveraged to refine predictions, specifically, RNA-Seq coverage to inform gHMM gene prediction, and intron lengths to inform protein sequence alignments. Second, a consensus is created from an empirically optimized weighting of gene models from multiple sources. Third, Eukan runs a post-annotation routine to recover gene models missing from the consensus that otherwise have strong transcript support and appear to be protein-coding. We compare the results of Eukan with those of three popular freely-available pipelines (Maker, Braker, Gemoma) on 17 phylogenetically diverse haploid and diploid nuclear genomes. In addition to the commonly reported annotation accuracy statistics, we define a novel classification system of critical defects commonly observed in automated annotations. Furthermore, we developed a statistical model that demonstrates each of the tested pipelines correctly identified the majority of the validated “Gold Standard” gene models across the test set, but each pipeline uniquely generates a non-negligible portion of either fragmented, artificially fused, or missing gene models. Despite that, we demonstrate that Eukan performs consistently well where other pipelines encounter challenges, such as for compact protist genomes. Contact Matt Sarrasin; matt.sarrasin@umontreal.ca
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".