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Record W1985103004 · doi:10.1186/1471-2164-15-737

Characterization of the core and accessory genomes of Pseudomonas aeruginosa using bioinformatic tools Spine and AGEnt

2014· article· en· W1985103004 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMC Genomics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesUniversité LavalNational Institutes of HealthAmerican Cancer Society
KeywordsGenomeBiologyWhole genome sequencingPseudomonas aeruginosaComparative genomicsGeneticsComputational biologyBacterial genome sizeGeneGenomicsBacteria

Abstract

fetched live from OpenAlex

BACKGROUND: Pseudomonas aeruginosa is an important opportunistic pathogen responsible for many infections in hospitalized and immunocompromised patients. Previous reports estimated that approximately 10% of its 6.6 Mbp genome varies from strain to strain and is therefore referred to as "accessory genome". Elements within the accessory genome of P. aeruginosa have been associated with differences in virulence and antibiotic resistance. As whole genome sequencing of bacterial strains becomes more widespread and cost-effective, methods to quickly and reliably identify accessory genomic elements in newly sequenced P. aeruginosa genomes will be needed. RESULTS: We developed a bioinformatic method for identifying the accessory genome of P. aeruginosa. First, the core genome was determined based on sequence conserved among the completed genomes of twelve reference strains using Spine, a software program developed for this purpose. The core genome was 5.84 Mbp in size and contained 5,316 coding sequences. We then developed an in silico genome subtraction program named AGEnt to filter out core genomic sequences from P. aeruginosa whole genomes to identify accessory genomic sequences of these reference strains. This analysis determined that the accessory genome of P. aeruginosa ranged from 6.9-18.0% of the total genome, was enriched for genes associated with mobile elements, and was comprised of a majority of genes with unknown or unclear function. Using these genomes, we showed that AGEnt performed well compared to other publically available programs designed to detect accessory genomic elements. We then demonstrated the utility of the AGEnt program by applying it to the draft genomes of two previously unsequenced P. aeruginosa strains, PA99 and PA103. CONCLUSIONS: The P. aeruginosa genome is rich in accessory genetic material. The AGEnt program accurately identified the accessory genomes of newly sequenced P. aeruginosa strains, even when draft genomes were used. As P. aeruginosa genomes become available at an increasingly rapid pace, this program will be useful in cataloging the expanding accessory genome of this bacterium and in discerning correlations between phenotype and accessory genome makeup. The combination of Spine and AGEnt should be useful in defining the accessory genomes of other bacterial species as well.

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.

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 score0.424
Threshold uncertainty score0.269

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)0.0000.000

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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