Pooled CRISPR Inverse PCR sequencing (PCIP-seq): simultaneous sequencing of retroviral insertion points and the associated provirus in thousands of cells with long reads
Bibliographic record
Abstract
Abstract Retroviral infections create a large population of cells, each defined by a unique proviral insertion site. Methods based on short-read high throughput sequencing can identify thousands of insertion sites, but the proviruses within remain unobserved. We have developed Pooled CRISPR Inverse PCR sequencing (PCIP-seq), a method that leverages long reads on the Oxford Nanopore MinION platform to sequence the insertion site and its associated provirus. We have applied the technique to three exogenous retroviruses, HTLV-1, HIV-1 and BLV, as well as endogenous retroviruses in both cattle and sheep. The long reads of PCIP-seq improved the accuracy of insertion site identification in repetitive regions of the genome. The high efficiency of the method facilitated the identification of tens of thousands of insertion sites in a single sample. We observed thousands of SNPs and dozens of structural variants within proviruses and uncovered evidence of viral hypermutation, recombination and recurrent selection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".