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Record W2790696000 · doi:10.1021/acs.jnatprod.7b00769

Lichen Biosynthetic Gene Clusters. Part I. Genome Sequencing Reveals a Rich Biosynthetic Potential

2018· article· en· W2790696000 on OpenAlexafffund
Robert L. Bertrand, Mona Abdel‐Hameed, John L. Sorensen

Bibliographic record

VenueJournal of Natural Products · 2018
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyketideBiologyNonribosomal peptidePolyketide synthaseSecondary metabolismLichenGeneGene clusterGenomeNatural productSecondary metaboliteFungusBiosynthesisGeneticsBiochemistryBotany

Abstract

fetched live from OpenAlex

Lichens are symbionts of fungi and algae that produce diverse secondary metabolites with useful properties. Little is known of lichen natural product biosynthesis because of the challenges of working with lichenizing fungi. We describe the first attempt to comprehensively profile the genetic secondary metabolome of a lichenizing fungus. An Illumina platform combined with the Antibiotics and Secondary Metabolites Analysis Shell (FungiSMASH, version 4.0) was used to sequence and annotate assembled contigs of the fungal partner of Cladonia uncialis. Up to 48 putative gene clusters are described comprising type I and type III polyketide synthases (PKS), nonribosomal peptide synthetases (NRPS), hybrid PKS-NRPS, and terpene synthases. The number of gene clusters revealed by this work dwarfs the number of known secondary metabolites from C. uncialis, suggesting that lichenizing fungi have an unexplored biosynthetic potential.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2018
Admission routes2
Has abstractyes

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