Table 1.
Summarised comparison of CarveMe, gapseq, and ModelSEED
| Metric | CarveMe | gapseq | ModelSEED |
|---|---|---|---|
| Implementation | |||
| Infrastructure | Local | Local | Web service |
| Input (FASTA file) | Protein | Nucleotide | Nucleotide |
| Programming languages | Python | Shell script, R | Perl/javascript |
| Gap-fill solver | CPLEX | GLPK/CPLEX | Not needed* |
| Gap-fill problem formulation | MILP | LP | MILP |
| Performance | |||
| Accuracy | 0.66 | 0.80 | 0.69 |
| Sensitivity | 0.34 | 0.71 | 0.33 |
| Specificity | 0.85 | 0.82 | 0.88 |
| Model file quality** | 0.32±0.006 | 0.78±0.004 | 0.39±0.016 |
Accuracy, sensitivity, and specificity scores are based on 14,931 tested phenotypes including energy sources, enzyme activity, fermentation products, gene essentiality, and anaerobic food web structure predictions.
*Solver runs on ModelSEED server. No local solver is required.
**MEMOTE total score mean (± SD).