TECHNICAL ADVANCE: Indel arrays: an affordable alternative for genotyping
Bibliographic record
Abstract
Natural variation and induced mutations are important resources for gene discovery and the elucidation of genetic circuits. Mapping such polymorphisms requires rapid and cost-efficient methods for genome-wide genotyping. Here we report the development of a microarray-based method that assesses 240 unique markers in a single hybridization experiment at a cost of less than US$50 in materials per line. Our genotyping array is built with 70-mer oligonucleotide elements representing insertion/deletion (indel) polymorphisms between the Arabidopsis thaliana accessions Columbia-0 (Col) and Landsberg erecta (Ler). These indel polymorphisms are recognized with great precision by comparative genomic hybridization, eliminating the need for array replicates and complex statistical analysis. Markers are present genome-wide, with an average spacing of approximately 500 kb. PCR primer information is provided for all array indels, allowing rapid single-locus inquiries. Multi-well chips allow groups of 16 lines to be genotyped in a single experiment. We demonstrate the utility of the array for accurately mapping recessive mutations, RIL populations and mixed genetic backgrounds from accessions other than Col and Ler. Given the ease of use of shotgun sequencing to generate partial genomic sequences of unsequenced species, this approach is readily transferable to non-model organisms.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.038 |
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".