MétaCan
Menu
Back to cohort
Record W2343181984 · doi:10.1186/s12864-016-2604-7

Consensus pan-genome assembly of the specialised wine bacterium Oenococcus oeni

2016· article· en· W2343181984 on OpenAlexfundno aff
Peter R. Sternes, Anthony R. Borneman

Bibliographic record

VenueBMC Genomics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersWine AustraliaAustralian GovernmentAlberta Water Research Institute
KeywordsOenococcus oeniBiologyGenomeWineBacteriaGeneticsComputational biologyMalolactic fermentationDNA microarrayGeneGene expression

Abstract

fetched live from OpenAlex

BACKGROUND: Oenococcus oeni is a lactic acid bacterium that is specialised for growth in the ecological niche of wine, where it is noted for its ability to perform the secondary, malolactic fermentation that is often required for many types of wine. Expanding the understanding of strain-dependent genetic variations in its small and streamlined genome is important for realising its full potential in industrial fermentation processes. RESULTS: Whole genome comparison was performed on 191 strains of O. oeni; from this rich source of genomic information consensus pan-genome assemblies of the invariant (core) and variable (flexible) regions of this organism were established. Genetic variation in amino acid biosynthesis and sugar transport and utilisation was found to be common between strains. Furthermore, we characterised previously-unreported intra-specific genetic variations in the natural competence of this microbe. CONCLUSION: By assembling a consensus pan-genome from a large number of strains, this study provides a tool for researchers to readily compare protein-coding genes across strains and infer functional relationships between genes in conserved syntenic regions. This establishes a foundation for further genetic, and thus phenotypic, research of this industrially-important species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.735

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.223
Teacher spread0.189 · 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

Citations42
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBMC GenomicsSame topicFermentation and Sensory AnalysisFrench-language works237,207