Profiling of extensively diversified plant <scp>LINE</scp>s reveals distinct plant‐specific subclades
Bibliographic record
Abstract
A large fraction of eukaryotic genomes is made up of long interspersed nuclear elements (LINEs). Due to their capability to create novel copies via error-prone reverse transcription, they generate multiple families and reach high copy numbers. Although mammalian LINEs have been well described, plant LINEs have been only poorly investigated. Here, we present a systematic cross-species survey of LINEs in higher plant genomes shedding light on plant LINE evolution as well as diversity, and facilitating their annotation in genome projects. Applying a Hidden Markov Model (HMM)-based analysis, 59 390 intact LINE reverse transcriptases (RTs) were extracted from 23 plant genomes. These fall in only two out of 28 LINE clades (L1 and RTE) known in eukaryotes. While plant RTE LINEs are highly homogenous and mostly constitute only a single family per genome, plant L1 LINEs are extremely diverse and form numerous families. Despite their heterogeneity, all members across the 23 species fall into only seven L1 subclades, some of them defined here. Exemplarily focusing on the L1 LINEs of a basal reference plant genome (Beta vulgaris), we show that the subclade classification level does not only reflect RT sequence similarity, but also mirrors structural aspects of complete LINE retrotransposons, like element size, position and type of encoded enzymatic domains. Our comprehensive catalogue of plant LINE RTs serves the classification of highly diverse plant LINEs, while the provided subclade-specific HMMs facilitate their annotation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".