High‐Throughput Analysis of Alternative Splicing by RT‐PCR
Bibliographic record
Abstract
As the importance of alternative splicing (AS) in biology becomes increasingly evident, so too does the need to explore biological systems with ever-higher precision and depth. At the RNA level, microarray – and, more recently, deep-sequencing (RNA-Seq) – techniques have emerged to meet this challenge, but the underlying requirement of data validation, most commonly using RT-PCR methods, can be a limiting factor. A rapid and simple technique based on endpoint PCR has been developed for the precise characterization of defined alternative splicing events. This technique has been adapted to a high-throughput automated platform, which can be used routinely to perform and analyze up to 3000 PCR reactions per day. This allows large-scale validations of genome-wide microarray or RNA-Seq data, and the analysis of sequence databases for the discovery and annotation of tissue-specific alternative splicing. The key features of this method are the computational design and data analysis protocols, and the use of microcapillary electrophoresis to detect and characterize the AS events. The platform is available to the scientific community on both, a fee-for-service and collaborative basis.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.019 |
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".