Comparative Genomic Hybridization on BAC Arrays
Bibliographic record
Abstract
Alterations in genomic DNA are a key feature of many constitutional disorders and cancer. The discovery of the underlying regions of gene dosage has thus been essential in dissecting complex disease phenotypes and identifying targets for therapeutic intervention and diagnostic testing. The development of array comparative genomic hybridization (aCGH) using bacterial artificial chromosomes (BACs) as hybridization targets has facilitated the discovery and fine mapping of novel genomic alterations allowing rapid identification of target genes. In BAC aCGH, DNA samples are first labeled with fluorescent dyes through a random priming reaction with 100-400 ng of genomic DNA. This probe is then co-hybridized to an array consisting of BAC clones, either tiling the genome (approximately 50 kbp resolution) or spaced at intervals (e.g., 1 Mbp resolution). The resulting arrays are then imaged and the signal at each locus is compared between a reference and test sample to determine the copy number status. The DNA samples to be analyzed may be derived from either fresh, frozen, or formalin-fixed paraffin-embedded material, and sample requirements are currently significantly lower than those for oligonucleotide platforms due to the high probe-binding capacity of BAC clone targets (approximately 150 kbp) compared to oligonucleotides (25-80 bp). In this chapter, we describe in detail the technical procedure required to perform copy number analysis of genomes with BAC aCGH.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".