EzMap: a simple pipeline for reproducible analysis of the human virome
Bibliographic record
Abstract
SUMMARY: In solid-organ transplant recipients, a delicate balance between immunosuppression and immunocompetence must be achieved, which can be difficult to monitor in real-time. Shotgun sequencing of cell-free DNA (cfDNA) has been recently proposed as a new way to indirectly assess immune function in transplant recipients through analysis of the status of the human virome. To facilitate exploration of the utility of the human virome as an indicator of immune status, and to enable rapid, straightforward analyses by clinicians, we developed a fully automated computational pipeline, EzMap, for performing metagenomic analysis of the human virome. EzMap combines a number of tools to clean, filter, and subtract WGS reads by mapping to a reference human assembly. The relative abundance of each virus present is estimated using a maximum likelihood approach that accounts for genome size, and results are presented with interactive visualizations and taxonomy-based summaries that enable rapid insights. The pipeline is automated to run on both workstations and computing clusters for all steps. EzMap automates an otherwise tedious and time-consuming protocol and aims to facilitate rapid and reproducible insights from cfDNA. AVAILABILITY AND IMPLEMENTATION: EzMap is freely available at https://github.com/dekoning-lab/ezmap. CONTACT: jason.dekoning@ucalgary.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 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 | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".