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Record W2108634507 · doi:10.1186/1743-422x-2-30

Host-derived pathogenicity islands in poxviruses

2005· article· en· W2108634507 on OpenAlexaff
Melissa Da Silva, Chris Upton

Bibliographic record

VenueVirology Journal · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGenomeBiologyGeneVirulenceGeneticsGC-contentENCODE

Abstract

fetched live from OpenAlex

BACKGROUND: Poxviruses are important both as pathogens and as vaccine vectors. Poxvirus genomes (150-350 kb) consist of a single linear dsDNA molecule; the two polynucleotide strands are joined by short hairpin loops. The genomes encode highly conserved proteins required for DNA replication and mRNA transcription as well as a variable set of virulence factors; transcription takes place within the cytoplasm of the host cell. We are interested in evolution of poxvirus genomes and especially how these viruses acquire host-derived genes that are believed to function as virulence factors. RESULTS: Using a variety of bioinformatics tools, we have identified regions in poxvirus genomes that have unusual nucleotide composition (higher or lower than average A+T content) compared to the genome as a whole; such regions may be several kilobases in length and contain a number of genes. Regions with unusual nucleotide composition may represent genes that have been recently acquired from the host genome. The study of these genomic regions with unusual nucleotide content will help elucidate evolutionary processes in poxviruses. CONCLUSION: We have found that dotplots of complete poxvirus genomes can be used to locate regions on the genome that differ significantly in A+T content to the genome as a whole. The genes in these regions may have been acquired relatively recently from the host genome or from another AT-rich poxvirus.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2005
Admission routes1
Has abstractyes

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