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Record W2397369086 · doi:10.1385/1-59259-676-2:275

Purification and Depletion of RNP Particles by Antisense Affinity Chromatography

2003· article· en· W2397369086 on OpenAlexaff
Benjamin J. Blencowe, Angus I. Lamond

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsDe Beers (Canada)University of Toronto
Fundersnot available
KeywordssnRNPOligonucleotideRibonucleoproteinRNARNA splicingSmall nuclear ribonucleoproteinNucleaseAffinity chromatographyBiologyChemistryMolecular biologyBiochemistryDNAGene

Abstract

fetched live from OpenAlex

Antisense oligonucleotides made of 2′-O-alkyl RNA are useful reagents for the study of the structure and function of ribonucleoprotein (RNP) particles. The nuclease resistant properties of these oligonucleotides, coupled with their ability to form specific and stable hybrids with targeted RNA sequences in crude cellular extracts, makes them well suited for applications involving the biochemical characterization of RNP particles. 2′-O-alkyl RNA oligonucleotides were initially used in “antisense masking” experiments to investigate the function of individual snRNA domains in the pre-mRNA splicing process (1–3). The coupling of biotin residues to these oligonucleotides subsequently allowed their use as affinity “hooks” for the purification and removal of specific RNP particles from cellular extracts (2,3). The purification of specific RNPs by this methodology has resulted in the identification of new RNP proteins (4–10). The ability to deplete targeted RNPs has allowed the function of individual snRNP and non-snRNP splicing factors to be investigated (11–17). The antisense affinity technology is thus a powerful alternative to conventional chromatographic methods and, in principle, is applicable to any RNP particle that contains a specific sequence accessible to oligonucleotide binding.

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 score0.017
Threshold uncertainty score0.250

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 head0.028
GPT teacher head0.268
Teacher spread0.239 · 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

Citations18
Published2003
Admission routes1
Has abstractyes

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