Volcano: a pipeline to characterize long terminal repeat-retrotransposons families in plants
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".