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Record W2064753411 · doi:10.1186/1756-0500-2-254

Evidence of the adaptive evolution of immune genes in chicken

2009· article· en· W2064753411 on OpenAlexaff
Tim Downing, Paul Cormican, Cliona O’Farrelly, Daniel G. Bradley, Andrew T. Lloyd

Bibliographic record

VenueBMC Research Notes · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsTrinity College
FundersFood Institutional Research Measure
KeywordsNonsynonymous substitutionBiologyZebra finchGeneGeneticsEvolutionary biologyGenomeLineage (genetic)Most recent common ancestorPhylogenetic treeMolecular evolutionNegative selection

Abstract

fetched live from OpenAlex

The basis for understanding the characteristics of gene functional categories in chicken has been enhanced by the ongoing sequencing of the zebra finch genome, the second bird species to be extensively sequenced. This sequence provides an avian context for examining how variation in chicken has evolved since its divergence from its common ancestor with zebra finch as well as well as a calibrating point for studying intraspecific diversity within chicken. Immune genes have been subject to many selective processes during their evolutionary history: this gene class was investigated here in a set of orthologous chicken and zebra finch genes with functions assigned from the human ortholog. Tests demonstrated that nonsynonymous sites at immune genes were highly conserved both in chicken and on the avian lineage. McDonald-Kreitman tests provided evidence of adaptive evolution and a higher rate of selection on fixation of nonsynonymous substitutions at immune genes compared to that at non-immune genes. Further analyses showed that GC content was much higher in chicken than in zebra finch genes, and was significantly elevated in both species' immune genes. Pathogen challenges are likely to have driven the selective forces that have shaped variation at chicken immune genes, and continue to restrict diversity in this functional class.

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)0.001
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 score0.497
Threshold uncertainty score0.128

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.134
GPT teacher head0.369
Teacher spread0.235 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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