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Record W2152911380 · doi:10.1128/jb.01985-12

Comparative Analysis of Mobilizable Genomic Islands

2012· article· en· W2152911380 on OpenAlexaff
Aurélie Daccord, Daniela Ceccarelli, Sébastien Rodrigue, Vincent Burrus

Bibliographic record

VenueJournal of Bacteriology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversité de Sherbrooke
FundersUniversidade Federal do Rio de Janeiro
KeywordsBiologyGeneticsGenomic islandIntegrasePhylogenetic treePlasmidIntegrasesGenomeComparative genomicsGeneGenomicsComputational biology

Abstract

fetched live from OpenAlex

Mobilizable genomic islands (MGIs) are small genomic islands of less than 35 kbp containing an integrase gene and a sequence that resembles the origin of transfer (oriT) of an integrating conjugative element (ICE). MGIs have been shown to site-specifically integrate and excise from the chromosome of bacterial hosts and hijack the conjugative machinery of a coresident ICE to disseminate. To date, MGIs have been described in three strains belonging to three different Vibrio species. In this study, we report the discovery of 11 additional putative MGIs found in various species of Vibrio, Alteromonas, Pseudoalteromonas, and Methylophaga. We designed an MGI capture system that allowed us to relocate chromosomal MGIs onto a low-copy-number plasmid and facilitate their isolation and sequencing. Comparative genomics and phylogenetic analyses of these mobile genetic elements revealed their mosaic structure and their evolution through recombination and acquisition of exogenous DNA. MGIs were found to belong to a larger family of genomic islands (GIs) sharing a similar integrase gene and often integrated into the same integration site yet exhibiting a different mechanism of regulation of excision and mobilization. We found that MGIs can excise only when an ICE of the SXT/R391 family is coresident in the same cell, while GIs still excise regardless.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.025
GPT teacher head0.320
Teacher spread0.295 · 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 designObservational
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

Citations40
Published2012
Admission routes1
Has abstractyes

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