GoT-Splice protocol for multi-omics profiling of gene expression, cell-surface proteins, mutational status, and RNA splicing in human cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Studying RNA splicing factor mutations is challenging due to difficulties in distinguishing wild-type and mutant cells within complex human tissues and inaccuracies associated with reconstructing splicing signals from short-read sequencing data. Here, we present Genotyping of Transcriptomes (GoT)-Splice, a protocol that overcomes these limitations by combining GoT with enhanced long-read single-cell transcriptome and cell-surface proteomics profiling. We describe steps for long-read library preparation and analysis, followed by cDNA re-amplification, enrichment of mutation of interest, sample indexing, and GoT library preparation. For complete details on the use and execution of this protocol, please refer to Cortés-López et al.1
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it