MIP-MAP: High-Throughput Mapping of <i>Caenorhabditis elegans</i> Temperature-Sensitive Mutants via Molecular Inversion Probes
Bibliographic record
Abstract
Abstract Mutants remain a powerful means for dissecting gene function in model organisms such as Caenorhabditis elegans. Massively parallel sequencing has simplified the detection of variants after mutagenesis but determining precisely which change is responsible for phenotypic perturbation remains a key step. Genetic mapping paradigms in C. elegans rely on bulk segregant populations produced by crosses with the problematic Hawaiian wild isolate and an excess of redundant information from whole-genome sequencing (WGS). To increase the repertoire of available mutants and to simplify identification of the causal change, we performed WGS on 173 temperature-sensitive (TS) lethal mutants and devised a novel mapping method. The mapping method uses molecular inversion probes (MIP-MAP) in a targeted sequencing approach to genetic mapping, and replaces the Hawaiian strain with a Million Mutation Project strain with high genomic and phenotypic similarity to the laboratory wild-type strain N2. We validated MIP-MAP on a subset of the TS mutants using a competitive selection approach to produce TS candidate mapping intervals with a mean size < 3 Mb. MIP-MAP successfully uses a non-Hawaiian mapping strain and multiplexed libraries are sequenced at a fraction of the cost of WGS mapping approaches. Our mapping results suggest that the collection of TS mutants contains a diverse library of TS alleles for genes essential to development and reproduction. MIP-MAP is a robust method to genetically map mutations in both viable and essential genes and should be adaptable to other organisms. It may also simplify tracking of individual genotypes within population mixtures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".