MétaCan
Menu
Back to cohort
Record W2408419511 · doi:10.1007/978-1-62703-113-4_6

Native RNA Purification by Gel Filtration Chromatography

2012· article· en· W2408419511 on OpenAlexafffund
Evan P. Booy, Hui Meng, Sean A. McKenna

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaManitoba Health Research Council
KeywordsRNAT7 RNA polymeraseFast protein liquid chromatographySize-exclusion chromatographyRNA extractionTranscription (linguistics)DNARNA-dependent RNA polymeraseChemistryRecombinant DNAIn vitroPolymeraseRNA polymeraseMolecular biologyChromatographyBiologyComputational biologyBiochemistryHigh-performance liquid chromatographyGeneEnzymeBacteriophageEscherichia coli

Abstract

fetched live from OpenAlex

In vitro transcription of RNA from DNA templates by T7 RNA polymerase allows for the generation of large quantities of RNA suitable for many downstream applications. The resulting RNA can be purified by a number of methodologies. Herein, we describe the native isolation of RNA molecules by FPLC purification using Superdex 75 or 200 gel filtration columns. This approach can be extended to purify biologically interesting RNA complexes such as RNA-protein complexes that have been generated from either synthetic or in vitro transcribed RNAs and recombinant proteins.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.010

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 head0.390
Teacher spread0.370 · 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
GenreMethods

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

Citations20
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueMethods in molecular biologySame topicRNA Research and SplicingFrench-language works237,207