Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life
Bibliographic record
Abstract
ABSTRACT A genome-scale network reconstruction (GENRE) represents the knowledgebase of an organism and can be used in a variety of applications. The drop in genome sequencing costs has led to an increase in sequenced genomes, but the number of curated GENRE’ s has not kept pace. This gap hinders our ability to study physiology across the tree of life. Furthermore, our analysis of yeast GENRE’ s has found they contain significant commission and omission errors, especially in central metabolism. To address these quantity and quality issues for GENRE’ s, we propose open and transparent curation of the pan-genome, pan-reactome, pan-metabolome, and pan-phenome for taxons by research communities, rather than for a single species. We outline our approach with a Fungi pan-GENRE by integrating AYbRAH, our ortholog database, and AYbRAHAM, our new fungal reaction database. This pan-GENRE was used to compile 33 yeast/fungi GENRE’ s in the Dikarya subkingdom, spanning 600 million years. The fungal pan-GENRE contains 1547 orthologs, 2726 reactions, 2226 metabolites, and 10 compartments. The strain GENRE’ s have a wider genomic and metabolic than previous yeast and fungi GENRE’ s. Metabolic simulations show the amino acid yields from glucose differs between yeast lineages, indicating metabolic networks have evolved in yeasts. Curating ortholog and reaction databases for a taxon can be used to increase the quantity and quality of strain GENRE’ s. This pan-GENRE framework provides the ability to scale high-quality GENRE’ s to more 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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".