Reprever: resolving low-copy duplicated sequences using template driven assembly
Bibliographic record
Abstract
Genomic sequence duplication is an important mechanism for genome evolution, often resulting in large sequence variations with implications for disease progression. Although paired-end sequencing technologies are commonly used for structural variation discovery, the discovery of novel duplicated sequences remains an unmet challenge. We analyze duplicons starting from identified high-copy number variants. Given paired-end mapped reads, and a candidate high-copy region, our tool, Reprever, identifies (a) the insertion breakpoints where the extra duplicons inserted into the donor genome and (b) the actual sequence of the duplicon. Reprever resolves ambiguous mapping signatures from existing homologs, repetitive elements and sequencing errors to identify breakpoint. At each breakpoint, Reprever reconstructs the inserted sequence using profile hidden Markov model (PHMM)-based guided assembly. In a test on 1000 artificial genomes with simulated duplication, Reprever could identify novel duplicates up to 97% of genomes within 3 bp positional and 1% sequence errors. Validation on 680 fosmid sequences identified and reconstructed eight duplicated sequences with high accuracy. We applied Reprever to reanalyzing a re-sequenced data set from the African individual NA18507 to identify >800 novel duplicates, including insertions in genes and insertions with additional variation. polymerase chain reaction followed by capillary sequencing validated both the insertion locations of the strongest predictions and their predicted sequence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".