Rapid donor-specific single nucleotide variation detection by nanopore sequencing of ex vivo lung perfusate
Bibliographic record
Abstract
Background Donor-derived cell-free DNA (ddcfDNA) has been shown to be useful in monitoring lung graft health, and single nucleotide variations (SNVs) between donor and recipient are used to identify ddcfDNA in post-transplant recipient blood. One limitation is the need to map donor or recipient SNVs prior to calculating %ddcfDNA. In this study, we use Nanopore sequencing of ex vivo lung perfusion perfusate cfDNA to map donor SNVs and validate it using standard short-read whole genome sequencing (WGS). Methods: cfDNA was extracted from 11 clinical ex vivo lung perfusion perfusate samples and sequenced using a Nanopore sequencer. SNVs were identified by comparison to a reference genome and then filtered for homozygous calls overlapping the 1000 Genomes SNP database. Matching short-read WGS was performed on 6 matching samples to act as a gold standard. Following mapping, %ddcfDNA was calculated in cell-free DNA (cfDNA) collected from matching post-lung transplant recipient plasma using SNVs called by Nanopore vs SNVs called by short-read WGS and compared for accuracy. Results Nanopore sequencing yielded genomic data with a median coverage of 4.88x (range 2.27-8.79) using a single flow cell with a median run length of 72 hours. The median general error rate of the sequence was 5.55% (range 4.98%-6.49%), and the median number of SNV with a depth > 3 and overlap with the 1000 Genomes SNP database was 246,993 (range 99,357-313,721). The positive predictive value of SNVs identified using 2X to 6X coverage cutoffs ranged from 66.6% to 96.9%, with a median value of 90.3% at 6X coverage. Correlation analysis showed a strong correlation between results by Nanopore sequencing and results by WGS for detecting %ddcfDNA in post-transplant plasma (R^2 = 0.996, p < 0.001). Conclusions Despite the lower sequencing accuracy and depth obtained from Nanopore sequencing, a high positive predictive value can be achieved in a set of donor-specific SNVs when appropriately filtered by read depth and overlap with SNP databases. This demonstrates the potential for the use of Nanopore sequencing to generate personalized donor cfDNA maps for use in post-operative donor-derived plasma cfDNA identification.
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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 | 0.000 | 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) | 0.001 | 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 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".