Conserved intergenic sequences revealed by CTAG-profiling in Salmonella: thermodynamic modeling for function prediction
Bibliographic record
Abstract
Highly conserved short sequences help identify functional genomic regions and facilitate genomic annotation. We used Salmonella as the model to search the genome for evolutionarily conserved regions and focused on the tetranucleotide sequence CTAG for its potentially important functions. In Salmonella, CTAG is highly conserved across the lineages and large numbers of CTAG-containing short sequences fall in intergenic regions, strongly indicating their biological importance. Computer modeling demonstrated stable stem-loop structures in some of the CTAG-containing intergenic regions, and substitution of a nucleotide of the CTAG sequence would radically rearrange the free energy and disrupt the structure. The postulated degeneration of CTAG takes distinct patterns among Salmonella lineages and provides novel information about genomic divergence and evolution of these bacterial pathogens. Comparison of the vertically and horizontally transmitted genomic segments showed different CTAG distribution landscapes, with the genome amelioration process to remove CTAG taking place inward from both terminals of the horizontally acquired segment.
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.
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 | 0.000 |
| 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 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".