Introduction to the Analysis of Environmental Sequence Information Using Metapathways
Bibliographic record
Abstract
This chapter describes the capabilities of MetaPathways, a modular pipeline designed for metabolic pathway reconstruction and comparative analysis of environmental sequence information. It describes taxonomic and functional gene profiling using the lowest common ancestor (LCA) algorithm and MLTreeMap and the construction and exploration of environmental pathway/genome databases (ePGDBs) using Pathway Tools. The chapter presents useful comparative analysis methods in the R statistical environment. These include computationally intensive pipeline steps related to scalable seed-and-extend searches, data integration across multiple information levels, and interactive visualization of embarrassingly large data sets. The chapter highlights recent pipeline innovations related to several of these challenges including a grid and cloud distribution system, graphical user interface (GUI), and Knowledge Engine data structure that integrates millions of functional and taxonomic annotations across multiple samples. Future versions of MetaPathways will build on distributed computing and interactive visualization themes and append modules for single-cell genomic and population genome assembly and binning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.032 |
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".