Additional file 1 of PCIP-seq: simultaneous sequencing of integrated viral genomes and their insertion sites with long reads
Bibliographic record
Abstract
Additional file 1: Figure S1. Clonality pie charts in sheep and cattle. Figure S2. SNP validation by clone specific PCR. Figure S3. Distinguishing between real SNPs and technical artifacts. Figure S4. Hypermutation in BLV proviruses. Figure S5. Validation of BLV structural variants by clone specific PCR. Figure S6. SNPs and recombination in the HIV-1 cell line U1. Figure S7. Estimation of the efficiency of PCIP-seq. Figure S8. Examples of HIV-1 proviruses from patient 02006. Figure S9. Insertion site of ERV causing cholesterol deficiency in Holstein cattle. Figure S10. Validated BERVK2 identified via PCIP-seq. Figure S11. ERV BTA3_115.3 LTRs match APOB (BTA11_77.9) ERV. Figure S12. Validated enJSRV. Table S1. Comparing PCIP-seq to ligation mediated PCR and Illumina sequencing. Table S2. SNPs identified in each sample. Table S3. BLV structural variants identified by PCIP-seq. Table S4. HIV-1 patients’ clinical information. Table S5. BERVK2s identified in cattle by PCIP-seq. Table S6. enJSRVs identified in sheep by PCIP-seq. Table S7. HPV integration sites identified in patients HPV18_PX and HPV18_PY. Table S8. PCIP-seq efficiency estimation in BLV. Supplementary note 1. Rationale behind the use of CRISPR-cas9 to cleave circular DNA. Supplementary note 2. Effect of coverage on SNP calling. Supplementary Methods. Supplementary References.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.845 | 0.000 |
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 teacher head, 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".