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Record W2975784044 · doi:10.1093/jxb/erz435

Histone tales: lysine methylation, a protagonist in Arabidopsis development

2019· review· en· W2975784044 on OpenAlexafffund
Kai Cheng, Yingchao Xu, Chao Yang, Luc A. Ouellette, Longjian Niu, Xiaochen Zhou, Liutian Chu, Feng Zhuang, Jin Liu, Hualing Wu, Jean‐Benoît Charron, Ming Luo

Bibliographic record

VenueJournal of Experimental Botany · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsMcGill University
FundersYouth Innovation Promotion AssociationNational Key Research and Development Program of ChinaYouth Innovation Promotion Association of the Chinese Academy of SciencesChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsArabidopsisHistoneLysineMethylationHistone methyltransferaseHistone methylationEZH2Histone H3BiologyCell biologyChemistryDNA methylationBiochemistryGeneGene expressionAmino acid

Abstract

fetched live from OpenAlex

Histone methylation plays a fundamental role in the epigenetic regulation of gene expression driven by developmental and environmental cues in plants, including Arabidopsis. Histone methyltransferases and demethylases act as 'writers' and 'erasers' of methylation at lysine and/or arginine residues of core histones, respectively. A third group of proteins, the 'readers', recognize and interpret the methylation marks. Emerging evidence confirms the crucial roles of histone methylation in multiple biological processes throughout the plant life cycle. In this review, we summarize the regulatory mechanisms of lysine methylation, especially at histone H3 tails, and focus on the recent advances regarding the roles of lysine methylation in Arabidopsis development, from seed performance to reproductive development, and in callus formation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.074
GPT teacher head0.362
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations84
Published2019
Admission routes2
Has abstractyes

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