Estimating overannotation across prokaryotic genomes using BLAST+, UBLAST, LAST and BLAT
Bibliographic record
Abstract
BACKGROUND: As the number of genomes in public databases increases, it becomes more important to be able to quickly choose the best annotated genomes for further analyses in comparative genomics and evolution. A proxy to annotation quality is the estimation of overannotation by comparing annotated coding genes against the SwissProt database. NCBI's BLAST (BLAST+) is the common software of choice to compare these sequences. Newer programs that run in a fraction of the time as BLAST+ might miss matches that BLAST+ would find. However, the results might still be useful to calculate overannotation. We thus decided to compare the overannotation estimates yielded using three such programs, UBLAST, LAST and the Blast-Like Alignment Tool (BLAT), and to test non-redundant versions of the SwissProt database to reduce the number of comparisons necessary. FINDINGS: We found that all, UBLAST, LAST and BLAT, tend to produce similar overannotation estimates to those obtained with BLAST+. As would be expected, results varied the most from those obtained with BLAST+ in genomes with fewer proteins matching sequences in the SwissProt database. UBLAST was the fastest running algorithm, and showed the smallest variation from the results obtained using BLAST+. Reduced SwissProt databases did not seem to affect the results much, but the reduction in time was modest compared to that obtained from UBLAST, LAST, or BLAT. CONCLUSIONS: Despite faster programs miss sequence matches otherwise found by NCBI's BLAST, the overannotation estimates are very similar and thus these programs can be used with confidence for this task.
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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 | 0.001 | 0.001 |
| 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".