Comparison of DNA Extraction Methods for Microbial Community Profiling with an Application to Pediatric Bronchoalveolar Lavage Samples
Bibliographic record
Abstract
Barcoded amplicon sequencing is rapidly becoming a standard method for profiling microbial communities, including the human respiratory microbiome. While this approach has less bias than standard cultivation, several steps can introduce variation including the type of DNA extraction method used. Here we assessed five different extraction methods on pediatric bronchoalveolar lavage (BAL) samples and a mock community comprised of nine bacterial genera to determine method reproducibility and detection limits for these typically low complexity communities. Additionally, using the mock community, we were able to evaluate contamination and select a relative abundance cut-off threshold based on the geometric distribution that optimizes the trade off between detecting bona fide operational taxonomic units and filtering out spurious ones. Using this threshold, the majority of genera in the mock community were predictably detected by all extraction methods including the hard-to-lyse Gram-positive genus Staphylococcus. Differences between extraction methods were significantly greater than between technical replicates for both the mock community and BAL samples emphasizing the importance of using a standardized methodology for microbiome studies. However, regardless of method used, individual patients retained unique diagnostic profiles. Furthermore, despite being stored as raw frozen samples for over five years, community profiles from BAL samples were consistent with historical culturing results. The culture-independent profiling of these samples also identified a number of anaerobic genera that are gaining acceptance as being part of the respiratory microbiome. This study should help guide researchers to formulate sampling, extraction and analysis strategies for respiratory and other human microbiome 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".