Detecting copy number variation with mated short reads
Bibliographic record
Abstract
The development of high-throughput sequencing (HTS) technologies has opened the door to novel methods for detecting copy number variants (CNVs) in the human genome. While in the past CNVs have been detected based on array CGH data, recent studies have shown that depth-of-coverage information from HTS technologies can also be used for the reliable identification of large copy-variable regions. Such methods, however, are hindered by sequencing biases that lead certain regions of the genome to be over- or undersampled, lowering their resolution and ability to accurately identify the exact breakpoints of the variants. In this work, we develop a method for CNV detection that supplements the depth-of-coverage with paired-end mapping information, where mate pairs mapping discordantly to the reference serve to indicate the presence of variation. Our algorithm, called CNVer, combines this information within a unified computational framework called the donor graph, allowing us to better mitigate the sequencing biases that cause uneven local coverage and accurately predict CNVs. We use CNVer to detect 4879 CNVs in the recently described genome of a Yoruban individual. Most of the calls (77%) coincide with previously known variants within the Database of Genomic Variants, while 81% of deletion copy number variants previously known for this individual coincide with one of our loss calls. Furthermore, we demonstrate that CNVer can reconstruct the absolute copy counts of segments of the donor genome and evaluate the feasibility of using CNVer with low coverage datasets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".