MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We have optimized and extended the widely used annotation engine MAKER in order to better support plant genome annotation efforts. New features include better parallelization for large repeat-rich plant genomes, noncoding RNA annotation capabilities, and support for pseudogene identification. We have benchmarked the resulting software tool kit, MAKER-P, using the Arabidopsis (Arabidopsis thaliana) and maize (Zea mays) genomes. Here, we demonstrate the ability of the MAKER-P tool kit to automatically update, extend, and revise the Arabidopsis annotations in light of newly available data and to annotate pseudogenes and noncoding RNAs absent from The Arabidopsis Informatics Resource 10 build. Our results demonstrate that MAKER-P can be used to manage and improve the annotations of even Arabidopsis, perhaps the best-annotated plant genome. We have also installed and benchmarked MAKER-P on the Texas Advanced Computing Center. We show that this public resource can de novo annotate the entire Arabidopsis and maize genomes in less than 3 h and produce annotations of comparable quality to those of the current The Arabidopsis Information Resource 10 and maize V2 annotation builds.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it