A pipeline of programs for collecting and analyzing group II intron retroelement sequences from GenBank
Bibliographic record
Abstract
Background Accurate and complete identification of mobile elements is a challenging task in the current era of sequencing, given their large numbers and frequent truncations. Group II intron retroelements, which consist of a ribozyme and an intron-encoded protein (IEP), are usually identified in bacterial genomes through their IEP; however, the RNA component that defines the intron boundaries is often difficult to identify because of a lack of strong sequence conservation corresponding to the RNA structure. Compounding the problem of boundary definition is the fact that a majority of group II intron copies in bacteria are truncated. Results Here we present a pipeline of 11 programs that collect and analyze group II intron sequences from GenBank. The pipeline begins with a BLAST search of GenBank using a set of representative group II IEPs as queries. Subsequent steps download the corresponding genomic sequences and flanks, filter out non-group II introns, assign introns to phylogenetic subclasses, filter out incomplete and/or non-functional introns, and assign IEP sequences and RNA boundaries to the full-length introns. In the final step, the redundancy in the data set is reduced by grouping introns into sets of ≥95% identity, with one example sequence chosen to be the representative. Conclusions These programs should be useful for comprehensive identification of group II introns in sequence databases as data continue to rapidly accumulate.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".