Pan‐Genome‐Scale Network Reconstruction: Harnessing Phylogenomics Increases the Quantity and Quality of Metabolic Models
Bibliographic record
Abstract
A genome-scale network reconstruction (GENRE) is a knowledgebase for an organism and has various applications. Available genome sequences have risen in recent years, but the number of curated GENREs has not kept pace. Existing yeast GENREs contain significant commission and omission errors. Current practices limit the quantity and quality of GENREs. An open and transparent phylogenomic-driven framework is outlined to address these issues. The method is demonstrated with 33 yeasts and fungi in Dikarya. A pan-fungal metabolic network called FYRMENT (Fungal and Yeast Metabolic Network) (https://github.com/LMSE/FYRMENT) is created, and annotated with ortholog groups from AYbRAH (https://github.com/LMSE/AYbRAH). Metabolic models for lower-level taxons are compiled. The fungal pan-GENRE contains 1553 orthologs, 2759 reactions, 2251 metabolites. The GENREs have higher genomic and metabolic coverage than existing yeast and fungal GENREs created with other methods. Metabolic simulations show the maximum amino acid yields from glucose differs between yeast lineages, indicating metabolic networks have evolved. Curating genomes and reactions at higher taxonomic-levels increases the quantity and quality of GENREs than conventional approaches. This approach can scale to other branches in the tree of life.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".