MyBASE: a database for genome polymorphism and gene function studies of Mycobacterium
Bibliographic record
Abstract
BACKGROUND: Mycobacterial pathogens are a major threat to humans. With the increasing availability of functional genomic data, research on mycobacterial pathogenesis and subsequent control strategies will be greatly accelerated. It has been suggested that genome polymorphisms, namely large sequence polymorphisms, can influence the pathogenicity of different mycobacterial strains. However, there is currently no database dedicated to mycobacterial genome polymorphisms with functional interpretations. DESCRIPTION: We have developed a mycobacterial database (MyBASE) housing genome polymorphism data and gene functions to provide the mycobacterial research community with a useful information resource and analysis platform. Whole genome comparison data produced by our lab and the novel genome polymorphisms identified were deposited into MyBASE. Extensive literature review of genome polymorphism data, mainly large sequence polymorphisms (LSPs), operon predictions and curated annotations of virulence and essentiality of mycobacterial genes are unique features of MyBASE. Large-scale genomic data integration from public resources makes MyBASE a comprehensive data warehouse useful for current research. All data is cross-linked and can be graphically viewed via a toolbox in MyBASE. CONCLUSION: As an integrated platform focused on the collection of experimental data from our own lab and published literature, MyBASE will facilitate analysis of genome structure and polymorphisms, which will provide insight into genome evolution. Importantly, the database will also facilitate the comparison of virulence factors among various mycobacterial strains. MyBASE is freely accessible via http://mybase.psych.ac.cn.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.026 |
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".