DNA reference reagents isolate biases in microbiome profiling: a global multi-lab study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When profiling the human gut microbiome, technical biases introduced by analytical approaches impede translational research, reducing data reliability and study comparability. Here, through a global study involving 23 labs, we analyzed a wide range of sequencing and bioinformatic approaches for the taxonomic profiling of two well-defined DNA reference reagents (RRs) comprised of 20 common gut bacteria. Through both shotgun and 16S rRNA gene amplicon sequencing, we aimed to isolate sources of bias and understand their impact on microbiome profiling accuracy. Importantly, minimum quality criteria (MQC) were established and are used to evaluate profiling performance. We found that the variability of shotgun sequencing data sets was greater than that of 16S rRNA gene amplicon sequencing and isolated sources of bias in wet and dry lab steps, such as sequencing depth, primer and database choices, rarefaction, and 16S copy number adjustment. This study presents well-defined RRs and MQC to combat technical bias, paving the way for reliable and comparable microbiome research.IMPORTANCEThis benchmark paper highlights the true level of variability in microbiome data across the world and across sectors, underscoring the critical need for the use of WHO International DNA Gut Reference Reagents (RRs) to elevate the quality of data in microbiome research. This global study is the first of its kind, revealing the reality of the bias in the field, comprehensively testing methodologies used by leading laboratories across the world, but also providing avenues for workflow optimization, to accelerate innovation and translational research and move the field forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it