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Record W2891365730 · doi:10.2144/btn-2018-0073

Analysis of rRNA Synthesis using Quantitative Transcription Run-on (qTRO) in Yeast

2018· article· en· W2891365730 on OpenAlexfundno aff
Michał Koper, Seweryn Mroczek

Bibliographic record

VenueBioTechniques · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsnot available
FundersSchool of Life and Environmental Sciences, Deakin UniversityUniwersytet WarszawskiNarodowym Centrum NaukiUniversität ZürichInstitute of GeneticsDeakin UniversityWellcome Trust
KeywordsTranscription (linguistics)BiologyRibosomal RNAOligonucleotideRNA polymerase IIRNARNA polymerase ISaccharomyces cerevisiaeRNA polymeraseComputational biologyMolecular biologyGeneGeneticsGene expressionPromoter

Abstract

fetched live from OpenAlex

Comparative transcriptional analyses require appropriate and precise normalization. Here we describe a modified transcription run-on (TRO) method, named quantitative TRO (qTRO), that allows quantification of nascent transcription activity. The most critical improvement it introduces is a new standardization method for RNA isolation and hybridization steps, enabling transcription activity quantification and comparative biological analysis. We used this technique with chromatin immunoprecipitation to investigate RNA polymerase I (RNAPI) transcription activity and its rDNA gene profiles in Saccharomyces cerevisiae. We designed a set of new oligonucleotide probes complementary to nascent ribosomal RNA (rRNA) transcripts and standardized their hybridization strength. The qTRO method could be successfully implemented to study RNAPI transcription in response to oxidative stress and in two mutant strains with impaired rRNA synthesis.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.436

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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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