Table 3.
Average alignment SP-error and tree error across fragmentary datasets
| Method | ROSE NT | RNASim 10K | Indelible 10K | CRW |
|---|---|---|---|---|
| (16S.3 and 16S.T) | ||||
| Average alignment SP-error | ||||
| UPP | 8.3 (1) | 11.8 (1) | 2.7 (1) | 16.1 (1) |
| PASTA | 25.2 (2) | 47.7 (4) | 8.8 (2) | 23.3 (2) |
| MAFFT | 32.5 (3) | 25.5 (2) | 51.3 (3) | 24.5 (3) |
| Muscle | 35.3 (4) | 82.2 (5) | 77.6 (4) | 70.6 (5) |
| Clustal | 62.0 (5) | 35.0 (3) | X | 46.7 (4) |
| Average ΔFN error | ||||
| UPP | 1.9 (1) | 3.1 (1) | 2.5 (1) | 7.4 (2) |
| PASTA | 25.2 (3) | 21.9 (3) | 9.0 (2) | 8.2 (2) |
| MAFFT | 18.0 (2) | 6.2 (2) | 35.6 (3) | 2.5 (1) |
| Muscle | 27.5 (4) | 43.6 (5) | 45.2 (4) | 30.1 (3) |
| Clustal | 47.8 (5) | 26.3 (4) | X | 37.4 (4) |
We report the average alignment error (top) and average ΔFN error (bottom) on the collection of fragmentary datasets. Clustal-Omega failed to align any of the Indelible 10000M2 fragmentary datasets and thus we mark the results with an X. The tier ranking for each method is shown in parentheses