RiboSnake – a user-friendly, robust, reproducible, multipurpose and documentation-extensive pipeline for 16S rRNA gene microbiome analysis
Bibliographic record
Abstract
Abstract Background Next-generation sequencing for assaying microbial communities has become a standard technique in recent years. However, the initial investment required into in-silico analytics is still quite significant, especially for facilities not focused on bioinformatics. With the rapid decline in costs and growing adoption of sequencing-based methods in a number of fields, validated, fully automated, reproducible and yet flexible pipelines will play a greater role in various scientific fields in the future. Results We present RiboSnake, a validated, automated, reproducible QIIME2-based analysis pipeline implemented in Snakemake for the computational analysis of 16S rRNA gene amplicon sequencing data. The pipeline comes with pre-packaged validated parameter sets, optimized for different sample types. The sets range from complex environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input. Conclusion RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and yet user-friendly pipeline for microbiome analysis with in-vitro validated settings. The complete analysis generated with a fully automated pipeline based on validated parameter sets for different sample types is a significant improvement to existing methods. The workflow repository can be found on GitHub ( https://github.com/IKIM-Essen/RiboSnake ).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.027 |
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".