Phoenix 2: A locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface
Bibliographic record
Abstract
We have developed Phoenix 2, a ribosomal RNA gene sequence analysis pipeline, which can be used to process large-scale datasets consisting of more than one hundred environmental samples and containing more than one million reads collectively. Rapid handling of large datasets is made possible by the removal of redundant sequences, pre-partitioning of sequences, parallelized clustering per partition, and subsequent merging of clusters. To build the pipeline, we have used a combination of open-source software tools and custom-developed Perl scripts. For our project we utilize hardware-accelerated searches, but it is possible to reconfigure the analysis pipeline for use with generic computing infrastructure only, with a considerable reduction in speed. The set of analysis results produced by Phoenix 2 is comprehensive, including taxonomic annotations using multiple methods, alpha diversity indices, beta diversity measurements, and a number of visualizations. To date, the pipeline has been used to analyze more than 1500 environmental samples from a wide variety of microbial communities, which are part of our Hydrocarbon Metagenomics Project (http://www.hydrocarbonmetagenomics.com). The software package can be installed as a local software suite with a Web interface. Phoenix 2 is freely available from http://sourceforge.net/projects/phoenix2.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.029 |
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".