Phoenix 2: A locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it