Genome wide identification of regulatory motifs in Bacillus subtilis
Bibliographic record
Abstract
BACKGROUND: To explain the vastly different phenotypes exhibited by the same organism under different conditions, it is essential that we understand how the organism's genes are coordinately regulated. While there are many excellent tools for predicting sequences encoding proteins or RNA genes, few algorithms exist to predict regulatory sequences on a genome wide scale with no prior information. RESULTS: To identify motifs involved in the control of transcription, an algorithm was developed that searches upstream of operons for improbably frequent dimers. The algorithm was applied to the B. subtilis genome, which is predicted to encode for approximately 200 DNA binding proteins. The dimers found to be over-represented could be clustered into 317 distinct groups, each thought to represent a class of motifs uniquely recognized by some transcription factor. For each cluster of dimers, a representative weight matrix was derived and scored over the regions upstream of the operons to predict the sites recognized by the cluster's factor, and a putative regulon of the operons immediately downstream of the sites was inferred. The distribution in number of operons per predicted regulon is comparable to that for well characterized transcription factors. The most highly over-represented dimers matched sigmaA, the T-box, and sigmaW sites. We have evidence to suggest that at least 52 of our clusters of dimers represent actual regulatory motifs, based on the groups' weight matrix matches to experimentally characterized sites, the functional similarity of the component operons of the groups' regulons, and the positional biases of the weight matrix matches. All predictions are assigned a significance value, and thresholds are set to avoid false positives. Where possible, we examine our false negatives, drawing examples from known regulatory motifs and regulons inferred from RNA expression data. CONCLUSIONS: We have demonstrated that in the case of B. subtilis our algorithm allows for the genome wide identification of regulatory sites. As well as recovering known sites, we predict new sites of yet uncharacterized factors. Results can be viewed at http://www.physics.rockefeller.edu/~mwangi/.
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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) | 0.000 | 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 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".