Microarray-based ultra-high resolution discovery of genomic deletion mutations
Bibliographic record
Abstract
BACKGROUND: Oligonucleotide microarray-based comparative genomic hybridization (CGH) offers an attractive possible route for the rapid and cost-effective genome-wide discovery of deletion mutations. CGH typically involves comparison of the hybridization intensities of genomic DNA samples with microarray chip representations of entire genomes, and has widespread potential application in experimental research and medical diagnostics. However, the power to detect small deletions is low. RESULTS: Here we use a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection. We show that the power to detect smaller deletions (4, 28 and 104 bp) depends upon oligonucleotide density (essentially the number of genome-representative oligonucleotides on the microarray chip), and determine the oligonucleotide spacings necessary to guarantee detection of deletions of specified size. CONCLUSIONS: Our findings will enhance a wide range of research and clinical applications, and in particular will aid in the discovery of genomic deletions in the absence of a priori knowledge of their existence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".