1. Application of Phylogenetic Networks in Evolutionary Studies Daniel H. Huson and David Bryant Presented by Peggy Wang

2. The plan  Terminology  Split networks: What are they? How can they be interpreted? Phylogenetic inference  SplitsTree4

3. A tree of terms..

4. Terminology  phylogenetic network any network in which taxa are represented by nodes and their evolutionary relationships are represented by edges

5. Types of networks  phylogenetic tree Leaf labeled tree that represents the evolutionary history of a set of taxa, possibly with branch(edge) lengths, either unrooted or rooted.  reticulate network Phylogenetic tree + additional edges. Nodes with more than two parents represent reticulate events such as hybridization, recombination and hgt  split network Represents incompatible and ambiguous signals in a data set. Parallel edges represents splits computed from data. Incompatible splits may result in nodes that do not represent ancestral species.

6. Wait… whats a split again?  split A partition of the taxa into two nonempty subsets, such as the partition obtained when we remove a branch from a phylogenetic tree.  split network (formally) For a given taxon set X and set of splits S, we define a split network N to be a connected graph in which some of the nodes are labeled by taxa and all edges are labeled by splits.

7.  Removing all edges associated with a given split s in S divides N into two connected components, one part containing all taxa on one side of S and the other part containing all taxa on the other side.  The edges along any shortest path in N are all associated with different splits.  A split network contains exactly the same information as a list of splits with a weight for each split.

8.  Every split network represents a unique collection of splits.  A given collection of splits can have many different split network representations.  The interpretation of the network depends on how the splits were constructed and assigned weights…

9. Interpreting Split Networks: Representing multiple trees  We can use split networks to summarize a large collection of trees. Code each individual tree as a collection of splits Define a summary set of splits Represent the set using a split network.  Consensus networks Constructed from all splits appearing in at least some fixed proportion of input trees

10. Interpreting Split Networks: Representing multiple trees  Confidence sets Assign an interval for the weights of each split A tree is contained within the split network N if (1)Every split in the tree is a split in the network (2)For every split in the tree, the corresponding branch length is contained within the corresponding interval (3)For every split in the network not in the tree, the assigned interval contains zero.. N

11. Interpreting Split Networks: Representing multiple trees Geometric interpretation: Index splits from 1 to m. Tree can be coded as a point in m-dimensional space: the ith coordinate is the length of the ith split, or 0 if that split is not present in the tree.

12. Interpreting Splits Networks: Networks and systematic error  Sampling error Random error resulting from a small sample size (number of sites). Deal with these errors using nonparametric bootstrap, multiple samples from posterior distribution  Systematic error Mistakes in the assumptions of a model or method which cause data to be misinterpreted. Likely to occur with large, multigene, heterogeneous data sets. How to deal with these errors?

13. Interpreting Splits Networks: Networks and systematic error  Phylogenetic inference (1)Construct a split network using the best available model and method. (2)Determine if the network is significantly different from a tree. (3)If the tree is significantly non-treelike, then there is probably an error in the model. If possible, improve the model and try again. (4)If the network is treelike, and there is no significant sampling error, the continue with a tree-based phylogenetic analysis.

14. Reticulate Networks 2 disagreeing trees Split network represents all splits present in either of the two trees Reticulate network Explains the differences in the two trees using 3 reticulation events

15. SplitsTree4  Integrates a wide range of phylogenetic network and phylogenetic tree methods, inference tools, data management utilities, and validation methods.  Included methods for inferring split networks: From character data. Median networks, parsimony splits, spectral analysis From distance matrices. Split decomposition and neighbor-net From sets of trees. Consensus networks and supernetworks.  Also constructs other types of phylogenetic networks, eg recombination and hybridization networks  User friendly?!

16. Example 1: Heterogeneous Evolution Jukes-Cantor p=0.75 q=0.05 0<r<0.4

17. Example 2: Animal Phylogeny Coelomate hypothesis Ecdysozoa hypothesis

18. More examples.. Dusky dolphins 60 variables (sites of DNA) 35 haplotypes Neighbor-joining tree with bootstrap values Consensus network of 3 MP trees Split decomposition network 95% confidence networkMedian networkNeighbor-net network

19. Conclusion!  Split networks are useful for visualization.  However they are not useful for making conclusive phylogenetic analysis.  SplitsTree4 encompasses many tools, but are they really that useful?