In Silico Proteomic Functional Re-annotation of <i>Escherichia coli</i> K-12 using Dynamic Biological Data Fusion Strategy
Bibliographic record
Abstract
Escherichia coli , one of the favorite model organisms, was initially annotated in 1997 and re-annotated in 2007. Although years of intensive research is being carried out on E. coli genome, still complete and accurate functional information of this organism is not available. In E. coli , about 40% of the protein sequences have been annotated as hypothetical proteins, because of lack of information. Hence, such sequences require advanced computational strategies and derive clues on their biological role. Herein, we have carried out re-annotation of the complete genome of E. coli K-12 using “Dynamic biological data fusion method”. It is a computational strategy we typically applied for combining the heterogeneous biological data sources to maximize knowledge sharing and generating the intersection of data sets. Functional re-annotation results reported in this paper help us to present high quality data on complete proteome of E. coli K-12. We have updated all the protein coding genes from previous annotation work and tried to assign new or more precise functions, wherever possible. About 29% of the protein sequences of E. coli which have been previously annotated as unclear / unknown (hypothetical; without functions) have now been assigned with clear / known functions. Further, the analysis also resulted in the revision of the protein sequences that have been found to be false positive or poorly annotated. Information from this work is made available as a database, “REC-DB, which will remain a useful repository with accurate and updated functional information. Availability: REC-DB is publicly available at http://192.168.2.168/recdb/index.html
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".