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Record W2789111892 · doi:10.1139/gen-2017-0174

Genome-wide identification, characterization, and transcriptional analysis of the metacaspase gene family in cucumber (<i>Cucumis sativus</i>)

2018· article· en· W2789111892 on OpenAlexvenueno aff
Yong Zhou, Lifang Hu, Lunwei Jiang, Shiqiang Liu

Bibliographic record

VenueGenome · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangxi ProvinceYouth Science Foundation of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsCucumisBiologyGeneIntronGene familyGeneticsGenomeGene expressionSequence analysisPhylogenetic treeMolecular biologyBotany

Abstract

fetched live from OpenAlex

Metacaspase (MC), a family of caspase-like proteins, plays vital roles in regulating programmed cell death (PCD) during development and in response to stresses in plants. In this study, five MC genes (designated as CsMC1 to CsMC5) were identified in the cucumber (Cucumis sativus) genome. Sequence analysis revealed that CsMC1-CsMC3 belong to type I MC proteins, while CsMC4 and CsMC5 are type II MC proteins. Phylogenetic tree and conserved motif analysis of MC proteins indicated that these proteins can be classified into two groups, which are correlated with the types of these MC proteins. Gene structure analysis demonstrated that type I CsMC genes contain 4-7 introns, while all type II CsMC genes harbor one intron. In addition, many hormone-, stress-, and development-related cis-elements were identified in the promoter regions of CsMC genes. Expression analysis using RNA-seq data revealed that CsMC genes have distinct expression patterns in various tissues and developmental stages. qRT-PCR results showed that the transcript levels of CsMC genes could be regulated by various abiotic stresses such as NaCl, PEG, and cold. These results demonstrate that the cucumber MC gene family may function in tissue development and plant 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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.434

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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 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

Citations15
Published2018
Admission routes1
Has abstractyes

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