Numerous studies covering some aspects of SARS-CoV-2 data analyses are being published on a daily basis, including a regularly updated phylogeny on nextstrain.org. Here, we review the difficulties of inferring reliable phylogenies by example of a data snapshot comprising a quality-filtered subset of 8, 736 out of all 16, 453 virus sequences available on May 5, 2020 from gisaid.org. We find that it is difficult to infer a reliable phylogeny on these data due to the large number of sequences in conjunction with the low number of mutations. We further find that rooting the inferred phylogeny with some degree of confidence either via the bat and pangolin outgroups or by applying novel computational methods on the ingroup phylogeny does not appear to be credible. Finally, an automatic classification of the current sequences into sub-classes using the mPTP tool for molecular species delimitation is also, as might be expected, not possible, as the sequences are too closely related. We conclude that, although the application of phylogenetic methods to disentangle the evolution and spread of COVID-19 provides some insight, results of phylogenetic analyses, in particular those conducted under the default settings of current phylogenetic inference tools, as well as downstream analyses on the inferred phylogenies, should be considered and interpreted with extreme caution.

In phylogenetic analysis, it is common to infer unrooted trees. Thus, it is unknown which node is the most recent common ancestor of all the taxa in the phylogeny. However, knowing the root location is desirable for downstream analyses and interpretation. There exist several methods to recover a root, such as midpoint rooting or rooting the tree at an outgroup. Non-reversible Markov models can also be used to compute the likelihood of a potential root position. We present a software called RootDigger which uses a non-reversible Markov model to compute the most likely root location on a given tree and to infer a confidence value for each possible root placement. Availability and implementation: RootDigger is available under the MIT licence at https://github.com/computations/root_digger

Phylogenetic analyses under the Maximum Likelihood model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating Maximum Likelihood based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyses, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard to-analyze datasets.

Background In phylogenetic analysis, it is common to infer unrooted trees. However, knowing the root location is desirable for downstream analyses and interpretation. There exist several methods to recover a root, such as molecular clock analysis (including midpoint rooting) or rooting the tree using an outgroup. Non-reversible Markov models can also be used to compute the likelihood of a potential root position. Results We present a software called which uses a non-reversible Markov model to compute the most likely root location on a given tree and to infer a confidence value for each possible root placement. We find that is successful at finding roots when compared to similar tools such as IQ-TREE and MAD, and will occasionally outperform them. Additionally, we find that the exhaustive mode of is useful in quantifying and explaining uncertainty in rooting positions. Conclusions can be used on an existing phylogeny to find a root, or to asses the uncertainty of the root placement. is available under the MIT licence at https://www.github.com/computations/root_digger.

Phylogenetic analyses under the Maximum Likelihood model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating Maximum Likelihood based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyses, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.