Depletion of Human DNA in Spiked Clinical Specimens for Improvement of Sensitivity of Pathogen Detection by Next-Generation Sequencing
Bibliographic record
Abstract
Next-generation sequencing (NGS) technology has shown promise for the detection of human pathogens from clinical samples. However, one of the major obstacles to the use of NGS in diagnostic microbiology is the low ratio of pathogen DNA to human DNA in most clinical specimens. In this study, we aimed to develop a specimen-processing protocol to remove human DNA and enrich specimens for bacterial and viral DNA for shotgun metagenomic sequencing. Cerebrospinal fluid (CSF) and nasopharyngeal aspirate (NPA) specimens, spiked with control bacterial and viral pathogens, were processed using either a commercially available kit (MolYsis) or various detergents followed by DNase prior to the extraction of DNA. Relative quantities of human DNA and pathogen DNA were determined by real-time PCR. The MolYsis kit did not improve the pathogen-to-human DNA ratio, but significant reductions (>95%;P< 0.001) in human DNA with minimal effect on pathogen DNA were achieved in samples that were treated with 0.025% saponin, a nonionic surfactant. Specimen preprocessing significantly decreased NGS reads mapped to the human genome (P< 0.05) and improved the sensitivity of pathogen detection (P< 0.01), with a 20- to 650-fold increase in the ratio of microbial reads to human reads. Preprocessing also permitted the detection of pathogens that were undetectable in the unprocessed samples. Our results demonstrate a simple method for the reduction of background human DNA for metagenomic detection for a broad range of pathogens in clinical samples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".