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Record W4390590447 · doi:10.1099/mgen.0.001167

Micromonosporaceae biosynthetic gene cluster diversity highlights the need for broad-spectrum investigations

2024· article· en· W4390590447 on OpenAlexaff
Imraan Alas, Doug R. Braun, Spencer S. Ericksen, Rauf Salamzade, Lindsay Kalan, Scott R. Rajski, Tim S. Bugni

Bibliographic record

VenueMicrobial Genomics · 2024
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsHealth Sciences CentreMcMaster University Medical Centre
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Center for Complementary and Integrative HealthNational Cancer InstituteNational Institutes of Health
KeywordsComputational biologyBiologyGene clusterMetric (unit)Diversity (politics)Natural productEvolutionary biologyGeneGeneticsEngineering

Abstract

fetched live from OpenAlex

Investigations of the bacterial family Micromonosporaceae have enabled the development of secondary metabolites critical to human health. Historical investigation of bacterial families for natural product discovery has focused on terrestrial strains, where time-consuming isolation processes often lead to the rediscovery of known compounds. To investigate the secondary metabolite potential of marine-derived Micromonosporaceae , 38 strains were sequenced, assembled and analysed using antiSMASH and BiG-SLiCE. BiG-SLiCE contains a near-comprehensive dataset of approximately 1.2 million publicly available biosynthetic gene clusters from primarily terrestrial strains. Our marine-derived Micromonosporaceae were directly compared to BiG-SLiCE’s preprocessed database using BiG-SLiCE’s query mode; genetic diversity within our strains was uncovered using BiG-SCAPE and metric multidimensional scaling analysis. Our analysis of marine-derived Micromonosporaceae emphasizes the clear need for broader genomic investigations of marine strains to fully realize their potential as sources of new natural products.

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)0.000
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 score0.326
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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