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Record W2761790783 · doi:10.1016/j.dib.2017.10.019

Annotating and quantifying pri-miRNA transcripts using RNA-Seq data of wild type and serrate-1 globular stage embryos of Arabidopsis thaliana

2017· article· en· W2761790783 on OpenAlexaff
Daniel Lepe-Soltero, Alma Armenta-Medina, Daoquan Xiang, Raju Datla, C. Stewart Gillmor, Cei Abreu‐Goodger

Bibliographic record

VenueData in Brief · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsPlant Biotechnology Institute
FundersCentro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional
KeywordsBiologyArabidopsis thalianaRNADicerEmbryoMutantGeneticsSmall RNARNA-SeqGenomeGeneComputational biologyGene expressionTranscriptomeRNA interference

Abstract

fetched live from OpenAlex

The genome annotation for the model plant Arabidopsis thaliana does not include the primary transcripts from which MIRNAs are processed. Here we present and analyze the raw mRNA sequencing data from wild type and serrate-1 globular stage embryos of A. thaliana, ecotype Columbia. Because SERRATE is required for pri-miRNA processing, these precursors accumulate in serrate-1 mutants, facilitating their detection using standard RNA-Seq protocols. We first use the mapping of the RNA-Seq reads to the reference genome to annotate the potential primary transcripts of MIRNAs expressed in the embryo. We then quantify these pri-miRNAs in wild type and serrate-1 mutants. Finally, we use differential expression analysis to determine which are up-regulated in serrate-1 compared to wild type, to select the best candidates for bona fide pri-miRNAs expressed in the globular stage embryos. In addition, we analyze a previously published RNA-Seq dataset of wild type and dicer-like 1 mutant embryos at the globular stage [1]. Our data are interpreted and discussed in a separate article [2].

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)0.001
metaresearch head score (Gemma)0.001
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 score0.795
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
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 head0.280
GPT teacher head0.372
Teacher spread0.092 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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