Comparative Genome Hybridization on Tiling Microarrays to Detect Aneuploidies in Yeast
Bibliographic record
Abstract
Chromosomal aberrations resulting in aneuploidies have been implicated in the development of most cancers and numerous other genetic disorders. Aneuploidies are a key feature of genomic instability, so classification of these copy number changes will be important in understanding how rearrangements arise and how ongoing instability is maintained. Traditional methods for detecting copy number changes have relatively poor resolution, making accurate detection of breakpoints impossible. The advent of microarray technology and its advance over the years has improved the ability to detect aneuploidies with greater accuracy. Mammalian comparative genome hybridization on microarrays (array-CGH) has been applied to the study of many carcinomas, identifying common copy number changes in key regions including known oncogenes. However, the large size of mammalian genomes has made it impractical to perform whole genome CGH at high resolution. Yeast has been established as a useful model for studying pathways relevant to oncogenesis, particularly those that maintain the integrity of the genome. Given the smaller size of the yeast genome, oligonucleotide tiling arrays have been developed that allow for nucleotide resolution of the whole genome on a single chip. Here we describe in detail how to use these arrays to detect copy number variations in yeast. This method will be useful in many different studies, but particularly in monitoring and cataloguing the changes resulting from genetic instability.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".