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Record W2170484544 · doi:10.1139/cjm-2015-0418

Analysis of synonymous codon usage in spike protein gene of infectious bronchitis virus

2015· article· en· W2170484544 on OpenAlexvenueno aff
Aditi Makhija, Sachin Kumar

Bibliographic record

VenueCanadian Journal of Microbiology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsnot available
FundersIndian Institute of Technology Guwahati
KeywordsSpike ProteinGeneInfectious bronchitis virusBiologyCodon usage biasGeneticsVirusVirologyStart codonSpike (software development)CoronavirusCoronavirus disease 2019 (COVID-19)GenomeBase sequenceInfectious disease (medical specialty)Medicine

Abstract

fetched live from OpenAlex

Infectious bronchitis virus (IBV) is responsible for causing respiratory, renal, and urogenital diseases in poultry. IBV infection in poultry leads to high mortality rates in affected flocks and to severe economic losses due to a drop in egg production and a reduced gain in live weight of the broiler birds. IBV-encoded spike protein (S) is the major protective immunogen for the host. Although the functions of the S protein have been well studied, the factors shaping synonymous codon usage bias and nucleotide composition in the S gene have not been reported yet. In the present study, we analyzed the relative synonymous codon usage and effective number of codons (Nc) using the 53 IBV S genes. The major trend in codon usage variation was studied using correspondence analysis. The plot of Nc values against GC3 as well as the correlation between base composition and codon usage bias suggest that mutational pressure rather than natural selection is the main factor that determines the codon usage bias in the S gene. Interestingly, no association of aromaticity, degree of hydrophobicity, and aliphatic index was observed with the codon usage variation in IBV S genes. The study represents a comprehensive analysis of IBV S gene codon usage patterns and provides a basic understanding of the codon usage bias.

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 score0.699
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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