Purification and Depletion of RNP Particles by Antisense Affinity Chromatography
Bibliographic record
Abstract
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".