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Record W3092191423 · doi:10.1139/gen-2020-0007

Identification and expression analysis of chloroplast ribonucleoproteins (cpRNPs) in Arabidopsis and rice

2020· article· en· W3092191423 on OpenAlexvenueno aff
Jiawen Wu, Huimin Liu, Shan Lu, Jian Hua, Baohong Zou

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNational Science Foundation
KeywordsArabidopsisBiologyGeneArabidopsis thalianaGeneticsChloroplastGenomeAbiotic stressOryza sativaRibonucleoproteinGene expressionRNA splicingGene familyRNA

Abstract

fetched live from OpenAlex

Chloroplast ribonucleoproteins (cpRNPs) are implicated in splicing, editing, and stability control of chloroplast RNAs as well as in regulating development and stress tolerance. To facilitate a comprehensive understanding of their functions, we carried out a genome-wide identification, curation, and phylogenetic analysis of cpRNP genes in Oryza sativa (rice) and Arabidopsis thaliana (Arabidopsis). Ten cpRNP genes were identified in each of Arabidopsis and rice genomes based on the presence of two RRM (RNA-recognition motif) domains and an N-terminal chloroplast targeting signal peptide in the predicted proteins. These proteins are localized to chloroplasts. Gene expression analysis revealed that cpRNP genes have differential tissue expression patterns and some cpRNP genes are induced by abiotic stresses such as cold, heat, and drought. Taken together, our study provides a comprehensive annotation of the cpRNP gene family and their expression patterns in Arabidopsis and rice which will facilitate further studies on their roles in plant growth and stress responses.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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