Table 5.
Tree error rates under high fragmentation for the best placement-based method and two MSA-ML methods. We show FN rates (top) and FP rates (bottom); We show results for the three most accurate methods: the best placement-based pipeline (UPP(R)-pplacer), the best MSA-ML method (UPP(R)-RAxML), and a standard MSA-ML method (PASTA-RAxML). Each condition has 50% full-length sequences and 50% fragmentary sequences with an average 25% length. The best results for each model condition (within 1%) are shown in boldface. The error rates are averaged over 20 replicates for the simulated datasets.
| Method | 1000M1 | 1000M2 | 1000M3 | 1000M4 | RNASim | RNASim2 | 16S.M | 23S.M |
|---|---|---|---|---|---|---|---|---|
| FN Rate: | ||||||||
| PASTA-RAXML | 0.765 | 0.616 | 0.355 | 0.164 | 0.436 | 0.362 | 0.409 | 0.321 |
| UPP(R)-RAXML | 0.370 | 0.304 | 0.237 | 0.167 | 0.377 | 0.338 | 0.340 | 0.363 |
| UPP(R)-pplacer | 0.488 | 0.437 | 0.380 | 0.320 | 0.507 | 0.477 | 0.496 | 0.458 |
| FP Rate: | ||||||||
| PASTA-RAXML | 0.766 | 0.618 | 0.359 | 0.184 | 0.436 | 0.362 | 0.723 | 0.585 |
| UPP(R)-RAXML | 0.372 | 0.307 | 0.241 | 0.187 | 0.377 | 0.338 | 0.690 | 0.611 |
| UPP(R)-pplacer | 0.370 | 0.307 | 0.234 | 0.170 | 0.397 | 0.362 | 0.712 | 0.613 |