A comprehensive evaluation of the sl1p pipeline for 16S rRNA gene sequencing analysis
Bibliographic record
Abstract
BACKGROUND: Advances in next-generation sequencing technologies have allowed for detailed, molecular-based studies of microbial communities such as the human gut, soil, and ocean waters. Sequencing of the 16S rRNA gene, specific to prokaryotes, using universal PCR primers has become a common approach to studying the composition of these microbiota. However, the bioinformatic processing of the resulting millions of DNA sequences can be challenging, and a standardized protocol would aid in reproducible analyses. METHODS: The short-read library 16S rRNA gene sequencing pipeline (sl1p, pronounced "slip") was designed with the purpose of mitigating this lack of reproducibility by combining pre-existing tools into a computational pipeline. This pipeline automates the processing of raw 16S rRNA gene sequencing data to create human-readable tables, graphs, and figures to make the collected data more readily accessible. RESULTS: Data generated from mock communities were compared using eight OTU clustering algorithms, two taxon assignment approaches, and three 16S rRNA gene reference databases. While all of these algorithms and options are available to sl1p users, through testing with human-associated mock communities, AbundantOTU+, the RDP Classifier, and the Greengenes 2011 reference database were chosen as sl1p's defaults based on their ability to best represent the known input communities. CONCLUSIONS: sl1p promotes reproducible research by providing a comprehensive log file, and reduces the computational knowledge needed by the user to process next-generation sequencing data. sl1p is freely available at https://bitbucket.org/fwhelan/sl1p .
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.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".