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Record W2920982670 · doi:10.5539/jas.v11n4p76

Patterns of Codon Usage Bias in WRKY Genes of Brassica rapa and Arabidopsis thaliana

2019· article· en· W2920982670 on OpenAlexvenueno aff
Snigdha Srivastava, Sheetal Chanyal, Ashutosh Dubey, A. K. Tewari, Gohar Taj

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsnot available
FundersGovind Ballabh Pant University of Agriculture and Technology
KeywordsBrassica rapaWRKY protein domainArabidopsis thalianaBiologyCodon usage biasGeneticsArabidopsisGeneAbiotic stressGenome

Abstract

fetched live from OpenAlex

Codon usage bias (CUB) is defined as the selective and nonrandom use of synonymous codons by the organism for encoding the amino acids. One of the important plant transcription factor family is the ‘WRKY’ whose role has been investigated in the regulation of abiotic and biotic stress responses in plants. In this paper, the codon usage pattern of the WRKY transcription factor of the two important plant species Arabidopsis thaliana and Brassica rapa has been investigated. Various codon usage indices like ENc, CAI, correspondence analysis, RSCU analysis, neutrality plot and hierarchial clustering has been done. The GC codon status was high in Arabidopsis. The RSCU analysis of codons revealed that codons coding for arginine was maximum in both the plant species.Our results propose that natural selection was the main dominating factor guiding the evolution of different WRKY genes in both Arabidopsis thaliana and Brassica rapa.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2019
Admission routes1
Has abstractyes

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