AppIndels.com server: a web-based tool for the identification of known taxon-specific conserved signature indels in genome sequences. Validation of its usefulness by predicting the taxonomic affiliation of >700 unclassified strains of Bacillus species
Bibliographic record
Abstract
Taxon-specific conserved signature indels (CSIs) in genes/proteins provide reliable molecular markers (synapomorphies) for unambiguous demarcation of taxa of different ranks in molecular terms and for genetic, biochemical and diagnostic studies. Because of their predictive abilities, the shared presence of known taxon-specific CSIs in genome sequences has proven useful for taxonomic purposes. However, the lack of a convenient method for identifying the presence of known CSIs in genome sequences has limited their utility for taxonomic and other studies. We describe here a web-based tool/server (AppIndels.com) that identifies the presence of known and validated CSIs in genome sequences and uses this information for predicting taxonomic affiliation. The utility of this server was tested by using a database of 585 validated CSIs, which included 350 CSIs specific for ≈45 Bacillales genera, with the remaining CSIs being specific for members of the orders Neisseriales , Legionellales and Chlorobiales , family Borreliaceae , and some Pseudomonadaceae species/genera. Using this server, genome sequences were analysed for 721 Bacillus strains of unknown taxonomic affiliation. Results obtained showed that 651 of these genomes contained significant numbers of CSIs specific for the following Bacillales genera/families: Alkalicoccus , ‘Alkalihalobacillaceae’, Alteribacter , Bacillus Cereus clade, Bacillus Subtilis clade, Caldalkalibacillus , Caldibacillus , Cytobacillus , Ferdinandcohnia, Gottfriedia , Heyndrickxia , Lederbergia , Litchfieldia , Margalitia , Mesobacillus , Metabacillus , Neobacillus , Niallia , Peribacillus
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.021 |
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".