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Record W2899588460 · doi:10.1042/bsr20181485

Combination of transcriptome sequencing and iTRAQ proteome reveals the molecular mechanisms determining petal shape in herbaceous peony (<i>Paeonia lactiflora</i> Pall.)

2018· article· en· W2899588460 on OpenAlexaff
Yanqing Wu, Yuhan Tang, Yu Jiang, Daqiu Zhao, Jiali Shang, Jun Tao

Bibliographic record

VenueBioscience Reports · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPaeonia lactifloraPetalBiologyTranscriptomeHerbaceous plantComplementary DNAProteomeDNA sequencingRapid amplification of cDNA endsGeneComputational biologyGeneticsBotanyGene expressionMolecular cloning

Abstract

fetched live from OpenAlex

The molecular mechanisms controlling petal shape in a herbaceous peony, Paeonia lactiflora Pall., a popular high-grade cut flower worldwide, remain unclear. Here, we selected inner and outer petals from P. lactiflora ‘ZiFengyu’ with an anemone type as the study object. Using transcriptome sequencing and isobaric tags for relative and absolute quantitation proteome, 979 differentially expressed genes and 266 differentially expressed proteins were detected within the inner and outer petals. Of these, the present study identified a key gene APETALA2 that regulates flower shape development. In addition, we obtained a 1935 bp full-length cDNA sequence of APETALA2 by rapid amplification of cDNA ends amplification. Through further validation using quantitative real-time polymerase chain reaction and Western blot analysis, APETALA2 showed a markedly higher expression in outer than that in inner petals. Therefore, the present study indicates that the increased expression of APETALA2 contributes to the formation of petals in P. lactiflora.

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 score0.001
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
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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