Preparing multiplexed 16S rRNA gene amplicons (with fusion primers) for the Illumina MiSeq v1
Bibliographic record
Abstract
The following protocol is for the generation of paired-end sequencing reads of 16S rRNA gene (V4 or V4V5) amplicons with dual barcodes (i.e.: “indexes”) on the Illumina MiSeq machine using v3 600 cycle chemistry. We use this protocol to make MiSeq libraries from DNA extracted from a variety of environmental samples, including seawater, freshwater, and swabs from hosts or surfaces of interest. This protocol is modified from dx.doi.org/10.17504/protocols.io.4r3l277k3g1y/v1 and makes use of 'fusion' primers or PCR primers that include not only the primer sequence, but also the Illumina adapter and Nextera index. This allows samples to be indexed for amplicon sequencing using a single PCR rather than using a two-step PCR approach described here. Many thanks to André Comeau and the Integrated Microbiome Resource at Dalhousie University for so clearly describing methods and allowing for reproducibility. The resources provided here https://github.com/LangilleLab/microbiome_helper/wiki and in their publication https://journals.asm.org/doi/10.1128/msystems.00127-16 were instrumental in developing our in-house protocols. Note: This protocol leverages combinatorial dual indexes. For other Illumina instruments (e.g. NextSeq), unique dual indexes may improve data quality and reduce index hopping. For a bit more information about the difference between unique dual indexes and combinatorial dual indexes, check out this resource. If you have any questions, please don't hesitate to contact us!
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.064 |
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".