MétaCan
Menu
Back to cohort
Record W1987100788 · doi:10.1002/cbic.201402322

A Dual Reporter System for Detecting RNA Interactions in Bacterial Cells

2014· article· en· W1987100788 on OpenAlexaff
Kacper Kuryllo, Shahrzad Jahanshahi, Weijia Zhu, Eric D. Brown, Yingfu Li

Bibliographic record

VenueChemBioChem · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRNAReporter geneEscherichia coliBiologyGeneCell biologyRiboswitchYellow fluorescent proteinGreen fluorescent proteinBimolecular fluorescence complementationComputational biologyGene expressionChemistryGeneticsNon-coding RNA

Abstract

fetched live from OpenAlex

Detecting RNA-partner interactions in cells is often difficult due to a lack of suitable tools. Here we describe a dual reporter system capable of detecting intracellular interactions in which one of the partners is an RNA. The system utilizes two fluorescent proteins with similar maturation rates but distinct spectral properties, specifically cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP). By placing the CFP gene upstream and the YFP gene downstream of an RNA gene of interest, the production of YFP becomes sensitive to RNA-partner interaction, whereas the synthesis of CFP is not disturbed. Therefore, the RNA-partner interaction can be simply measured by the change in the ratio of fluorescence of YFP over CFP. The utility of our approach is demonstrated through verification of three known RNA-partner interactions in the model bacterium Escherichia coli. Our two-reporter strategy should be broadly useful to the study of RNA-targeted interactions in bacteria.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.010
GPT teacher head0.236
Teacher spread0.225 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueChemBioChemSame topicBacterial Genetics and BiotechnologyFrench-language works237,207