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Record W3133775082 · doi:10.1093/g3journal/jkab058

The <i>Mitragyna speciosa</i> (Kratom) Genome: a resource for data-mining potent pharmaceuticals that impact human health

2021· article· en· W3133775082 on OpenAlexafffund
Julia Brose, Kin H. Lau, Thu Thuy Thi Dang, John P. Hamilton, Lívia do Vale Martins, Britta Hamberger, Björn Hamberger, Jiming Jiang, Sarah E. O’Connor, C. Robin Buell

Bibliographic record

VenueG3 Genes Genomes Genetics · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAlkaloids: synthesis and pharmacology
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCollege of Engineering, Michigan State UniversityAgBioResearch, Michigan State UniversityMichigan State University
KeywordsGenomeSyntenyContigBiologyGeneSequence assemblyAlkaloidHeterologousOxindoleChromosomeComputational biologyGeneticsTranscriptomeBiochemistryBotany

Abstract

fetched live from OpenAlex

Mitragyna speciosa (kratom) produces numerous compounds with pharmaceutical properties including the production of bioactive monoterpene indole and oxindole alkaloids. Using a linked-read approach, a 1,122,519,462 bp draft assembly of M. speciosa "Rifat" was generated with an N50 scaffold size of 1,020,971 bp and an N50 contig size of 70,448 bp that encodes 55,746 genes. Chromosome counting revealed that "Rifat" is a tetraploid with a base chromosome number of 11, which was further corroborated by orthology and syntenic analysis of the genome. Analysis of genes and clusters involved in specialized metabolism revealed genes putatively involved in alkaloid biosynthesis. Access to the genome of M. speciosa will facilitate an improved understanding of alkaloid biosynthesis and accelerate the production of bioactive alkaloids in heterologous hosts.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.339
GPT teacher head0.516
Teacher spread0.176 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2021
Admission routes2
Has abstractyes

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