Expanding probe repertoire and improving reproducibility in human genomic hybridization
Bibliographic record
Abstract
Diagnostic DNA hybridization relies on probes composed of single copy (sc) genomic sequences. Sc sequences in probe design ensure high specificity and avoid cross-hybridization to other regions of the genome, which could lead to ambiguous results that are difficult to interpret. We examine how the distribution and composition of repetitive sequences in the genome affects sc probe performance. A divide and conquer algorithm was implemented to design sc probes. With this approach, sc probes can include divergent repetitive elements, which hybridize to unique genomic targets under higher stringency experimental conditions. Genome-wide custom probe sets were created for fluorescent in situ hybridization (FISH) and microarray genomic hybridization. The scFISH probes were developed for detection of copy number changes within small tumour suppressor genes and oncogenes. The microarrays demonstrated increased reproducibility by eliminating cross-hybridization to repetitive sequences adjacent to probe targets. The genome-wide microarrays exhibited lower median coefficients of variation (17.8%) for two HapMap family trios. The coefficients of variations of commercial probes within 300 nt of a repetitive element were 48.3% higher than the nearest custom probe. Furthermore, the custom microarray called a chromosome 15q11.2q13 deletion more consistently. This method for sc probe design increases probe coverage for FISH and lowers variability in genomic microarrays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".