MIP-MAP: High Throughput Mapping of <i>Caenorhabditis elegans</i> Temperature Sensitive Mutants via Molecular Inversion Probes
Bibliographic record
Abstract
Abstract Temperature sensitive (TS) alleles are important tools for the genetic and functional analysis of essential genes in many model organisms. While isolating TS alleles is not difficult, determining the TS-conferring mutation can be problematic. Even with whole-genome sequencing (WGS) data there is a paucity of predictive methods for identifying TS alleles from DNA sequence alone. We assembled 173 TS lethal mutants of Caenorhabditis elegans and used WGS to identify several hundred mutations per strain. We leveraged single molecule molecular inversion probes (MIPs) to sequence variant sites at high depth in the cross-progeny of TS mutants and a mapping strain with identified sequence variants but no apparent phenotypic differences from the reference N2 strain. By sampling for variants at ~1Mb intervals across the genome we genetically mapped mutant alleles at a resolution comparable to current standards in a process we call MIP-MAP. The MIP-MAP protocol, however, permits high-throughput sequencing of multiple TS mutation mapping libraries at less than 200K reads per library. Using MIP-MAP on a subset of TS mutants, via a competitive selection assay and standard recombinant mutant selection, we defined TS-associated intervals of 3Mb or less. Our results suggest this collection of strains contains a diverse library of TS alleles for genes involved in development and reproduction. MIP-MAP is a robust method to genetically map mutations in both viable and essential genes. The MIPs protocol should allow high-throughput tracking of genetic variants in any mixed population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".