Comparison of iSeq and Miseq in 16S rRNA sequencing-based human gut microbiome analysis
Bibliographic record
Abstract
Illumina's MiSeq platform is a common approach in 16S-based microbiome analysis. Such usage is self-perpetuating in that many studies seek to employ widely used approaches to facilitate comparison of their results to existing literature. Yet, a range of factors, including cost and equipment availability can necessitate alternate approaches. For example, use of a Nano kit, lowers reagent costs by over 60% while others may only have access to entry-level sequencers such as Illumina's iSeq. The extent to which these approaches would impact results and subsequently conclusions is unknown. Attempting to address this question from the literature is complicated in that various studies not only use distinct cohorts but also differ in the reagents/methodologies to used to isolate DNA and generate sequencing libraries. Hence, we sequenced a single 16S rRNA gene amplicon library derived from 60 fecal samples, collected during a dietary supplement intervention study via MiSeq, MiseqNano, and iSeq. We evaluated platform performance by several key measurements: alpha diversity, beta diversity, taxonomic composition, and differential taxonomic abundance analysis. We found that iSeq outperformed MiSeq-Nano in alpha diversity and differential abundance detection, while MiSeq-Nano provided better taxonomic resolution than iSeq. Most importantly, all platforms showed similar core biological patterns in alpha and beta diversity and overall taxonomic composition. Thus, MiSeq, MiseqNano, and iSeq are likely to yield the same biological conclusions although specific questions and logistical consideration may favor one of these approaches.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".