1. Current Approaches to Whole Genome Phylogenetic Analysis Hongli Li

2. Content Background Background Genome Evolution Genome Evolution Phylogenetic Analysis Phylogenetic Analysis Performing Statistical Tests Performing Statistical Tests Phylogenetic Networks Phylogenetic Networks Conclusion Conclusion

3. Phylogenetic Analysis Background Early attempts – Based on morphological characters Early attempts – Based on morphological characters Directly compare genes make more sense Directly compare genes make more sense Modern attempts – Using sequences from individual homologous genes Modern attempts – Using sequences from individual homologous genes A gene’s evolutionary history might not the same as the evolutionary history of its organisms A gene’s evolutionary history might not the same as the evolutionary history of its organisms Some genes that are sufficiently conserved across all interested species might not be identified Some genes that are sufficiently conserved across all interested species might not be identified

4. Genome Evolution Prokaryotes Prokaryotes Relatively Simple Relatively Simple Prokaryote evolutionary history cannot properly be represented by a tree Prokaryote evolutionary history cannot properly be represented by a tree Eukaryotes Eukaryotes More complicated More complicated Frequent inversions of small segments, gene duplication and loss and polyploidy events Frequent inversions of small segments, gene duplication and loss and polyploidy events Organellar Genomes Organellar Genomes Contain smaller and simpler mitochondrial genome Contain smaller and simpler mitochondrial genome Plant species have chloroplast genome Plant species have chloroplast genome

5. Genome Evolution (cont.) Model of Genome Evolution Model of Genome Evolution Nadeau – Taylor Model Nadeau – Taylor Model

6. Phylogenetic Analysis – Binary Character Encoding Binary Character Encoding Binary Character Encoding Encode the presence or absence of particular genes or protein families are obvious whereas gene order are not Encode the presence or absence of particular genes or protein families are obvious whereas gene order are not Many different approaches. Many different approaches. Nature restriction Nature restriction A gene cannot adjacent to more than two others A gene cannot adjacent to more than two others A evolutionary event will create two adjacent and break two A evolutionary event will create two adjacent and break two

7. Phylogenetic Analysis – Distance Methods Distance Methods Distance Methods Smallest number of evolutionary events between two gnomes Smallest number of evolutionary events between two gnomes Breakpoint Distance Breakpoint Distance The distance between two genome with unequal content is a problem The distance between two genome with unequal content is a problem There are several software available for distance analysis There are several software available for distance analysis

8. Phylogenetic Analysis – Maximum Parsimony Try to find minimum tree is NP-hard Try to find minimum tree is NP-hard Several attempts Several attempts Find “breakpoint phylogeny” – Easier to find the maximum parsimony tree but still NP-hard Find “breakpoint phylogeny” – Easier to find the maximum parsimony tree but still NP-hard Try to find the true maximum parsimony with improved algorithms and computing power Try to find the true maximum parsimony with improved algorithms and computing power Parsimony method has more advantages compared to distance methods Parsimony method has more advantages compared to distance methods But difficult to measure the accuracy of solutions But difficult to measure the accuracy of solutions

9. Phylogenetic Analysis – Other Methods Maximum Likelihood Maximum Likelihood Computationally prohibitive Computationally prohibitive Method of Invariants Method of Invariants Relies on having good estimates for the invariant function, which requires large dataset Relies on having good estimates for the invariant function, which requires large dataset Bayesian Analysis Bayesian Analysis The probability distributions involved can become extremely complicated The probability distributions involved can become extremely complicated

10. Performing Statistical Tests Performing Statistical Tests for Phylogenetic features is not straight forward in any situation Performing Statistical Tests for Phylogenetic features is not straight forward in any situation Re-sampling methods should preserve the gene order and should be used with caution since new error might introduced Re-sampling methods should preserve the gene order and should be used with caution since new error might introduced

11. Phylogenetic Networks When dealing with whole genomes and in particular prokaryotic genomes we need phylogenetic networks When dealing with whole genomes and in particular prokaryotic genomes we need phylogenetic networks Split graphs Split graphs Reticulograms Reticulograms Can express uncertainty in a tree or a lack of faith in the tree model of evolution Can express uncertainty in a tree or a lack of faith in the tree model of evolution Not suitable for representing phenomena such as horizontal transfer or allopolyploid events Not suitable for representing phenomena such as horizontal transfer or allopolyploid events

12. Conclusion Comparison of gene content are becoming commonplace but comparison gene order present a wider range of problems Comparison of gene content are becoming commonplace but comparison gene order present a wider range of problems It is important to focus on the data we already or will have It is important to focus on the data we already or will have Methods for whole genome phylogenetic analysis need to be robust against missing or inaccurate information Methods for whole genome phylogenetic analysis need to be robust against missing or inaccurate information