cpnDB: A Chaperonin Sequence Database
Bibliographic record
Abstract
Type I chaperonins are molecular chaperones present in virtually all bacteria, some archaea and the plastids and mitochondria of eukaryotes. Sequences of cpn60 genes, encoding 60-kDa chaperonin protein subunits (CPN60, also known as GroEL or HSP60), are useful for phylogenetic studies and as targets for detection and identification of organisms. Conveniently, a 549-567-bp segment of the cpn60 coding region can be amplified with universal PCR primers. Here, we introduce cpnDB, a curated collection of cpn60 sequence data collected from public databases or generated by a network of collaborators exploiting the cpn60 target in clinical, phylogenetic, and microbial ecology studies. The growing database currently contains approximately 2000 records covering over 240 genera of bacteria, eukaryotes, and archaea. The database also contains over 60 sequences for the archaeal Type II chaperonin (thermosome, a homolog of eukaryotic cytoplasmic chaperonin) from 19 archaeal genera. As the largest curated collection of sequences available for a protein-encoding gene, cpnDB provides a resource for researchers interested in exploiting the power of cpn60 as a diagnostic or as a target for phylogenetic or microbial ecology studies, as well as those interested in broader subjects such as lateral gene transfer and codon usage. We built cpnDB from open source tools and it is available at http://cpndb.cbr.nrc.ca.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.025 |
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".