MétaCan
Menu
Back to cohort

Unveiling the functional and evolutionary landscape of RNA editing in chicken using genomics and transcriptomics

2022· article· en· W4307033112 on OpenAlexaff
Yun-Mei Wang, Lingqun Ye, Mingshan Wang, Jin-Jin Zhang, Saber Khederzadeh, David M. Irwin, Xiao-Die Ren, Ya‐Ping Zhang, Dong‐Dong Wu

Bibliographic record

Venue动物学研究 · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsADARRNA editingBiologyRNATranscriptomeInosineGeneticsRNA-binding proteinEvolutionary biologyRiboswitchFunctional genomicsAdenosine deaminaseComputational biologyNon-coding RNAGeneAdenosineGenomeGenomicsGene expressionBiochemistry

Abstract

fetched live from OpenAlex

The evolutionary and functional features of RNA editing are well studied in mammals, cephalopods, and insects, but not in birds. Here, we integrated transcriptomic and whole-genomic analyses to exhaustively characterize the expansive repertoire of adenosine-to-inosine (A-to-I) RNA editing sites (RESs) in the chicken. In addition, we investigated the evolutionary status of the chicken editome as a potential mechanism of domestication. We detected the lowest editing level in the liver of chickens, compared to muscles in humans, and found higher editing activity and specificity in the brain than in non-neural tissues, consistent with the brain’s functional complexity. To a certain extent, specific editing activity may account for the specific functions of tissues. Our results also revealed that sequences critical to RES secondary structures remained conserved within avian evolution. Furthermore, the RNA editome was shaped by purifying selection during chicken domestication and most RESs may have served as a selection pool for a few functional RESs involved in chicken domestication, including evolution of nervous and immune systems. Regulation of RNA editing in chickens by adenosine deaminase acting on RNA (ADAR) enzymes may be affected by non-ADAR factors whose expression levels changed widely after <i>ADAR</i> knockdown. Collectively, we provide comprehensive lists of candidate RESs and non-ADAR-editing regulators in the chicken, thus contributing to our current understanding of the functions and evolution of RNA editing in animals.

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 score0.791
Threshold uncertainty score0.151

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venue动物学研究Same topicRNA regulation and diseaseFrench-language works237,207