Performance Evaluation of Whole-Genome Amplification Platforms for Clinical Next-Generation Sequencing with Minimal Nucleic Acid Input
Bibliographic record
Abstract
Background: Next-generation sequencing (NGS) is increasingly applied in clinical diagnostics; however, standard workflows are frequently challenged by insufficient DNA yields in diverse clinical scenarios. Although whole-genome amplification (WGA) is used to overcome this limitation, comparative performance data on WGA kits based on different amplification mechanisms under low-input conditions remain scarce. Methods: We systematically evaluated four commercial WGA platforms: REPLI-g (Qiagen), which employs multiple displacement amplification; PicoPLEX (Takara Bio) and SurePlex (Illumina), which utilize modified multiple annealing and looping-based amplification cycles (MALBAC); and ResolveDNA (BioSkryb Genomics), which uses primary template-directed amplification (PTA), using 100-pg and 1-ng DNA input. Performance was assessed by examining allelic dropout (ADO), chimerism, copy number variation (CNV), and the total DNA yield. Results: ResolveDNA showed the lowest ADO rates across input levels, whereas PicoPLEX offered the most accurate quantification for chimerism and CNV. REPLI-g had the highest DNA yield but exhibited marked amplification bias and ADO under ultra-low-input conditions. SurePlex demonstrated intermediate performance across all metrics. PicoPLEX and SurePlex showed consistent CNV detection and chimerism accuracy, whereas PTA-based ResolveDNA better preserved allelic balance. Conclusions: Each platform demonstrated specific strengths and limitations depending on analytical endpoints. Modified MALBAC-based platforms can perform optimally when quantitative accuracy is critical, such as in chimerism or CNV analysis, whereas PTA-based WGA can be preferred when allelic fidelity is essential. Our findings can help guide platform selection tailored to specific clinical applications using low-input NGS, including preimplantation testing or cell-free DNA analysis.
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".