Variant Effect Mapping Protocol Collection v1
Bibliographic record
Abstract
Advances in cellular engineering and sequencing have enabled Multiplexed Assays of Variant Effect (MAVEs), a technological approach to proactively measure the functional impact of all possible missense variants in a disease-associated gene, including variants not yet observed in the clinic. The resulting “lookup tables” of measured variant function are commonly referred to as variant effect maps. There is a growing international effort to generate a comprehensive atlas of variant effects, with value not only for defining sequence–structure–function relationships and supporting clinical variant interpretation, but also for demonstrating quantitative correlations between functional scores and disease phenotypes. The protocols in this collection are organized sequentially and guide the user through preparing the gene of interest with a stop codon, performing site-directed mutagenesis, validating a scalable human cell- and yeast-based functional assay, generating mutagenic libraries, and executing a MAVE in a human cell line or in yeast. These workflows enable the measurement of variant effectsen masseusing complementary readouts — including growth-based assays and fluorophore reporters — to assess outputs such as total enzymatic activity and protein stability.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.237 | 0.163 |
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".