Performance Assessment of Phylogenetic Inference Tools using PhyloSmew

Motivation The performance of phylogenetic inference tools is commonly evaluated using simulated as well as empirical sequence data alignments. An open question is how representative these alignments are with respect to those, commonly analyzed by users. Using the RAxMLGrove database, it is now possible to simulate DNA and amino acid sequences based on more than 70,000 representative RAxML and RAxML-NG tree inferences on empirical datasets conducted on the RAxML web servers. This allows to assess the phylogenetic tree inference accuracy of various inference tools based on more realistic and representative simulated alignments. Results To automate this process, we implement PhyloSmew, a tool for benchmarking phylogenetic inference tools. We use it to simulate approximately 20,000 Multiple Sequence Alignments (MSAs) based on representative empirical trees (in terms of signal strength) from RAxMLGrove. We subsequently analyze 5000 empirical MSAs from the TreeBASE database, to assess the inference accuracy of FastTree2, IQ-TREE2, and RAxML-NG. We find that on quantifiably difficult-to-analyze MSAs, all three tree inference tools perform poorly. Hence, the faster FastTree2 tool, constitutes a viable alternative to infer trees on difficult MSAs. We also find that there are substantial differences between accuracy results on simulated versus empirical data. Availability and Implementation The data underlying this article are available at https://github.com/angtft/PhyloSmew, https://cme.h-its.org/exelixis/material/accuracy-study/data.tar.gz.