Updating microbial genomic sequences: improving accuracy & innovation
Bibliographic record
Abstract
Many bacterial genome sequences completed using the Sanger method may contain assembly errors due in-part to low sequence coverage driven by cost. To illustrate the need for re-sequencing of pre-nextgen genomes and to validate sequenced genomes, we conducted a series of experiments, using high coverage sequencing data generated by a Illumina Miseq sequencer to sequence genomic DNAs of Bacteroides fragilis NCTC 9343, Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, Vibrio cholerae O1 biovar El Tor str. N16961, Bacillus halodurans C-125 and Caulobacter crescentus CB15, which had previously been sequenced by the Sanger method during the early 2000’s. This study revealed a number of discrepancies between the published assemblies and sequence read alignments for all five bacterial species, suggesting that the continued use of these error-containing genomes and their genetic information may contribute to false conclusions and/or incorrect future discoveries when they are used.
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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".