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Record W2896846789 · doi:10.1186/s40793-018-0331-1

Insights into the draft genome sequence of bioactives-producing Bacillus thuringiensis DNG9 isolated from Algerian soil-oil slough

2018· article· en· W2896846789 on OpenAlexafffund
Mohamed Seghir Daas, Albert Remus R. Rosana, Jeella Z. Acedo, Malika Douzane, Farida Natèche, Salima Kebbouche-Gana, John C. Vederas

Bibliographic record

VenueStandards in Genomic Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesUniversity of AlbertaAlberta InnovatesAgence Universitaire de la FrancophonieAlberta Innovates - Health SolutionsAlberta Innovates - Technology Futures
KeywordsBacillus thuringiensisBacillus cereusBiologyGenomeSensuCereusWhole genome sequencingMicrobiologyStrain (injury)BacteriocinBacillus (shape)AntimicrobialGeneGeneticsBacteriaBotany

Abstract

fetched live from OpenAlex

Bacillus thuringiensis is widely used as a bioinsecticide due to its ability to form parasporal crystals containing proteinaceous toxins. It is a member of the Bacillus cereus sensu lato , a group with low genetic diversity but produces several promising antimicrobial compounds. B. thuringiensis DNG9, isolated from an oil-contaminated slough in Algeria, has strong antibacterial, antifungal and biosurfactant properties. Here, we report the 6.06 Mbp draft genome sequence of B. thuringiensis DNG9. The genome encodes several gene inventories for the biosynthesis of bioactive compounds such as zwittermycin A, petrobactin, insecticidal toxins, polyhydroxyalkanoates and multiple bacteriocins. We expect the genome information of strain DNG9 will provide another model system to study pathogenicity against insect pests, plant diseases, and antimicrobial compound mining and comparative phylogenesis among the Bacillus cereus sensu lato group.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.275
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2018
Admission routes2
Has abstractyes

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