aPhyloGeo-Covid: A Web Interface for Reproducible Phylogeographic Analysis of SARS-CoV-2 Variation using Neo4j and Snakemake
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
The gene sequencing data, along with the associated lineage tracing and research data generated throughout the Coronavirus disease 2019 (COVID-19) pandemic, constitute invaluable resources that profoundly empower phylogeography research. To optimize the utilization of these resources, we have developed an interactive analysis platform called aPhyloGeo-Covid, leveraging the capabilities of Neo4j, Snakemake, and Python. This platform enables researchers to explore and visualize diverse data sources specifically relevant to SARS-CoV-2 for phylogeographic analysis. The integrated Neo4j database acts as a comprehensive repository, consolidating COVID-19 pandemic-related sequences information, climate data, and demographic data obtained from public databases, facilitating efficient filtering and organization of input data for phylogeographical studies. Presently, the database encompasses over 113,774 nodes and 194,381 relationships. Additionally, aPhyloGeo-Covid provides a scalable and reproducible phylogeographic workflow for investigating the intricate relationship between geographic features and the patterns of variation in diverse SARS-CoV-2 variants. The code repository of platform is publicly accessible on GitHub (https://github.com/tahiri-lab/iPhyloGeo/tree/iPhylooGeo-neo4j), providing researchers with a valuable tool to analyze and explore the intricate dynamics of SARS-CoV-2 within a phylogeographic context.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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