Next‐generation mapping of Arabidopsis genes
Bibliographic record
Abstract
Next-generation genomic sequencing technologies have made it possible to directly map mutations responsible for phenotypes of interest via direct sequencing. However, most mapping strategies proposed to date require some prior genetic analysis, which can be very time-consuming even in genetically tractable organisms. Here we present a de novo method for rapidly and robustly mapping the physical location of EMS mutations by sequencing a small pooled F₂ population. This method, called Next Generation Mapping (NGM), uses a chastity statistic to quantify the relative contribution of the parental mutant and mapping lines to each SNP in the pooled F₂ population. It then uses this information to objectively localize the candidate mutation based on its exclusive segregation with the mutant parental line. A user-friendly, web-based tool for performing NGM analysis is available at http://bar.utoronto.ca/NGM. We used NGM to identify three genes involved in cell-wall biology in Arabidopsis thaliana, and, in a power analysis, demonstrate success in test mappings using as few as ten F₂ lines and a single channel of Illumina Genome Analyzer data. This strategy can easily be applied to other model organisms, and we expect that it will also have utility in crops and any other eukaryote with a completed genome sequence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".