Volcano: a pipeline to characterize long terminal repeat-retrotransposons families in plants
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
Motivation: Long Terminal Repeat Retrotransposons (LTR-RTs) comprise a significant portion of repetitive sequences in numerous plant species. LTR-RTs hold considerable functional significance, as they can impact gene family functionality and contribute to the formation of new genes. Investigating the quantities and activities of LTR-RTs is essential for understanding species' evolutionary dynamics and the foundational mechanisms driving genome evolution. While current softwares can predict and initially classify LTR-RTs, there is a high need for more comprehensive and efficient software to fully characterize and quantify LTR-RTs during burst events and in subsequent detailed classification and quantification, especially given the surged demands of genome annotation. Results: In this study, we have developed a pipeline called Volcano to accurately classify LTR-RTs and characterize burst families in plants. To distinguish different clades of LTR-RTs, we have implemented an improved depth-first search algorithm. Volcano can also quantify LTR-RT expression using RNA-seq data. By analyzing LTR-RTs in three genomes from the Asteraceae family, we observed that larger genomes tend to contain a greater number of LTR-RTs, and our software effectively categorizes them at the clade level. Availability and implementation: The proposed Volcano compressor can be downloaded from https://github.com/Suosihe/volcano_LTR.
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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