1. 8. Lecture WS 2003/04Bioinformatics III1 Phylogenetic Prediction (of single genes) Material of this lecture taken from - chapter 6, DW Mount „Bioinformatics“ - A. Okas et al., Nature 425, 798 (2003) Genome-scale approaches to resolving incongruence in molecular phylogenies. A phylogenetic analysis of a family of related nucleic acid or protein sequences is a determination of how the family might have been derived during evolution. Placing the sequences as outer branches on a tree, the evolutionary relationships among the sequences are depicted.

2. 8. Lecture WS 2003/04Bioinformatics III2 3 main approaches in single-gene phylogeny - maximum parsimony - distance - maximum likelihood Popular programs: PHYLIP (phylogenetic inference package – J Felsenstein) PAUP (phylogenetic analysis using parsimony – Sinauer Assoc

3. 8. Lecture WS 2003/04Bioinformatics III3 Concept of evolutionary trees An evolutionary tree is a 2-dimensional graph showing evolutionary relationships among organisms, or in the case of sequences, in certain genes from separate organisms. nodes branches sequence A sequence B sequence C sequence D rooted tree unrooted tree sequence A sequence B sequence C sequence D length of branches reflects number of sequence changes. Often: assume uniform rate of mutations (molecular clock hypothesis).

4. 8. Lecture WS 2003/04Bioinformatics III4 Concept of evolutionary trees Number of substitutions in each branch is generally assumed to vary according to the Poisson distribution that gives the probability P n around an average number x : The number of possible trees increases very rapidly with the number of sequences: #sequences#rooted trees#unrooted trees 3 3 1 4 15 3 5 105 15 - 710395954 A B C D

5. 8. Lecture WS 2003/04Bioinformatics III5 Methods for Single-Gene Phylogeny Choose set of related sequences Obtain multiple sequence alignment Is there strong sequence similarity? Maximum parsimony methods Yes No Is there clearly recogniza- ble sequence similarity? Yes Distance methods No Maximum likelihood methods Analyze how well data support prediction

6. 8. Lecture WS 2003/04Bioinformatics III6 Maximum Parsimony Method Method predicts the evolutionary tree that minimizes the number of steps required to generate the observed variation in the sequences. Step 0Input: multiple sequence alignment Step 1For each aligned position, identify phylogenetic trees that require the smallest number of evolutionary changes to produce the observed sequence changes. Step 1.5Continue analysis for every position in the sequence alignment. Step 2Sequence variations at each site in the alignment are placed at the tips of the trees. Identify the tree (trees) that produce the smallest number of changes overall for all sequence positions. Because all possible trees are examined, method is best suited for sequences that are quite similar + for small number of sequences. It is guaranteed to find the best tree.

7. 8. Lecture WS 2003/04Bioinformatics III7 Example Sequence#Sequence position 123456789 1AAGAGTGCA 2AGCCGTGCG 3AGATATCCA 4AGAGATCCG These are 4 sequences giving 3 possible unrooted trees. E.g. trees for position 5: G G A A AG GG A A A A GG A A A A Informative sites: (1) must favor one tree over another (site 5 is informative, but sites 1, 6, 8 are not). (2) To be informative, a site must also have the same sequence character in at least two genomes (only sites 5, 7, and 9 are informative according to this rule). Combining sites 5, 7, and 9, the left tree is the best tree for these 4 sequences. Seq4Seq3Seq4Seq3 Seq2Seq1Seq2Seq1 Seq4Seq2 Seq3Seq1

8. 8. Lecture WS 2003/04Bioinformatics III8 Where maximum parsimony fails Parsimony can give misleading information when rates of sequence change vary in the different branches of a tree that are represented by the sequence data. GG AA Real tree: 2 long branches in which G has turned to A independently, possibly with some intermediate steps. Seq3Seq2 Seq4Seq1 G G A A Seq3Seq4 Seq2Seq1 In parsimony analysis rates of change along all branches of the tree are assumed equal. Therefore the tree predicted from parsimony will not be correct.

9. 8. Lecture WS 2003/04Bioinformatics III9 Distance methods The distance method employs the number of changes between each pair in a group of sequences to produce a phylogenetic tree of the group. The sequence pairs that have the smallest number of sequence changes between them are termed „neighbors“. On a tree, these sequences share a node or common ancestor position and are each joined to that node by branch. Goal of distance methods: identify tree that correctly positions neighbors and that also has branch lengths that reproduce the original data as closely as possible.  neighbor-joining algorithm, Fitch-Margoliash algorithm Finding the closest neighbors among a group of sequences by the distance method is often the first step in producing a multiple sequence alignment. E.g. ClustalW uses the neighbor-joining distance method.

10. 8. Lecture WS 2003/04Bioinformatics III10 Example sequence AA C G C G T T G G G C G A T G G C A A C sequence BA C G C G T T G G G C G A C G G T A A T sequence CA C G C A T T G A A T G A T G A T A A T sequence DA C A C A T T G A G T G A T A A T A A T distances beween sequencesdistance table n AB 3 n AC 7 n AD 8 n BC 6 n BD 7 n CD 3 A B C D 2 1 4 1 2 ABCD A-378 B--67 C---3 D----

11. 8. Lecture WS 2003/04Bioinformatics III11 Maximum likelihood approach Method uses probability calculations to find a tree that best accounts for the variation in a set of sequences. Similar to maximum parsimony method in that analysis is performed on each column of a multiple sequence alignment. All trees are considered. Because the rate of appearance of new mutations is very small, the more mutations are needed to fit a tree to the data, the less likely that tree. Start with an evolutionary model of sequence change that provides estimates of rates of substitution of one base for another (transitions and transversions). Base ACGT A-u(a  C +b  G +c  T )ua  C ub  G uc  T Cug  A -u(g  A +d  G +e  T )ud  G ue  T Guh  A uj  G -u(h  A +j  G +f  T )uf  T Tui  A uk  G ul  T -u(i  A +k  G +l  T )

12. 8. Lecture WS 2003/04Bioinformatics III12 Maximum likelihood approach Step1Align set of sequences Step2 Examine substitutions in each column for their fit to a set of trees that describe possible phylogenetic relationships among the sequences. Each tree has a certain likelihood based on the series of mutations that are required to give the sequence data. The probability of each tree is the product of the mutation rates in each branch of the tree, which itself is the product of the rate of substitution in each branch times the branch length. Advantage of maximum likelihood approach: allows to evaluate trees with variations in mutation rates in different lineages. Can be used for more diverse sequences. Disadvantage: computationally intense.

13. 8. Lecture WS 2003/04Bioinformatics III13 Resolve Incongruences in Phylogeny Many possible reasons that may make decisions on how to handle conflicts in larger sets of molecular data difficult. E.g. two genes with different evolutionary history (e.g. owing to hybridization or horizontal transfer) will necessarily give incongruent pictures while still depicting true histories. Here: compare genome sequence data for 7 Saccharomyces yeast species: S. cerevisae S. paradoxus S. mikatae S. kudriavzevii S. bayanus S. castelli S. kluyveri plus one outgroup fungus Candida albicans. Rokas et al. Nature 425, 798 (2003)

14. 8. Lecture WS 2003/04Bioinformatics III14 Resolve Incongruences in Phylogeny Identify orthologous genes to serve as phylogenetic markers: 106 genes which are distributed throughout the S. cerevisae genome on all 16 chromosomes and comprise a total length of 127026 nt = 42342 amino acids corresponding to roughly 1% of the genomic sequence and 2% of the predicted genes. Criteria to select genes spaced ca. every 40 kb: (1) genes have homologous sequence in each of the 8 species (2) genes have at least two homologous flanking syntenic genes (3) genes can be aligned over most of the protein. 3 types of analysis: - maximum likelihood (ML) analysis of nucleotide data - maximum parsimony (MP) analysis of nucleotide data - MP of the amino acid data Rokas et al. Nature 425, 798 (2003)

15. 8. Lecture WS 2003/04Bioinformatics III15 Resolve Incongruences in Phylogeny Align individual genes with ClustalW. Edit manually to exclude indels and areas of uncertain alignment  left with 76% of the sequence of each gene on average. Tree construction with PAUP by branch-and-bound algorithm which guarantees to find the optimal tree. Estimate tree reliability using non-parametric bootstrap re- sampling. Analysis of the 106 genes gave more than 20 alternative ML or MP trees. Generate 50% majority-rule consensus trees by bootstrapping. Next slide shows several strongly supported trees. Rokas et al. Nature 425, 798 (2003)

16. 8. Lecture WS 2003/04Bioinformatics III16 A method for testing how well a particular data set fits a model. E.g. the validity of the branch arrangement in a predicted phylogenetic tree can be tested by resampling columns in a multiple sequence alignment to create many new alignments. The appearance of a particular branch in trees generated from these resampled sequences can then be measured. Alternatively, a sequence may be left out of an analysis to determine how much the sequence influences the results of an analysis. Here: swap individual nucleotide sites or positions of genes (bootstrap replicas). Bootstrap analysis.

17. 8. Lecture WS 2003/04Bioinformatics III17 Alternative Tree topologies Single-gene data sets generate multiple, robustly supported alternative topologies. Representative alternative trees recovered from analyses of nucleotide data of 106 selected single genes and six commonly used genes are shown. The trees are the 50% majority-rule consensus trees from the genes YBL091C (a), YDL031W (b), YER005W (c), YGL001C (d), YNL155W (e) and YOL097C (f). These 6 genes were selected without consideration of their function. Maybe commonly used, well known genes of important functions provide a better resolution? Rokas et al. Nature 425, 798 (2003)

18. 8. Lecture WS 2003/04Bioinformatics III18 Results from the commonly used genes actin (g), hsp70 (h),  -tubulin (i), RNA polymerase II (j) elongation factor 1-  (k) and 18S rDNA (l). Numbers above branches indicate bootstrap values (ML on nucleotides/MP on nucleotides).  Same problem of alternative topologies as before. Alternative Tree topologies Rokas et al. Nature 425, 798 (2003)

19. 8. Lecture WS 2003/04Bioinformatics III19 The alternative phylogenies could have resulted from a number of different scenarios: (1) most genes could have weakly supported most phylogenies and strongly supported only a few alternative trees, (2) most genes could have strongly supported one phylogeny and a few genes strongly supported only a small number of alternatives, (3) there could have been some combinations of these scenarios so that each branch among alternative phylogenies had either weak or strong support depending on the gene. To distinguish between these possibilities, identify all branches recovered during single-gene analyses, record each bootstrap value with respect to the gene and method of analysis.  8 branches were shared by all three analyses with multiple instances of bootstrap values > 50%. Explanations? Rokas et al. Nature 425, 798 (2003)

20. 8. Lecture WS 2003/04Bioinformatics III20 Common Branches The distribution of bootstrap values for the eight prevalent branches recovered from 106 single-gene analyses highlights the pervasive conflict among single- gene analyses. a, Majority-rule consensus tree of the 106 ML trees derived from single-gene analyses. Across all analyses, there were eight commonly observed branches; the five branches in the consensus tree (numbers 1–5; a) and the three branches (numbers 6–8) shown in b. Rokas et al. Nature 425, 798 (2003)

21. 8. Lecture WS 2003/04Bioinformatics III21 c, For each of the eight branches, the ranked distribution of per cent bootstrap values recovered from the three analyses of 106 genes is shown. Results from ML (blue) and MP (red) analyses of nucleotide data sets, and MP analyses of amino acid data sets (black), are shown. For each branch, the mean bootstrap value and 95% confidence intervals from the ML analyses and the percentage of ML trees supporting this branch (in parentheses) are indicated below each graph. Although the ranked distributions of bootstrap values from the three analyses are remarkably similar for most branches, on a gene-by-gene basis there is no tight correspondence between bootstrap values from ML and MP analyses Bootstrap Values of Common Branches Only branches 1 and 4 are supported by a majority of genes. Rokas et al. Nature 425, 798 (2003)

22. 8. Lecture WS 2003/04Bioinformatics III22 How different are the trees? The degree of conflict among the trees could be relatively minor. Determine how many taxa (genes) would need to be removed to make two trees congruent (deckungsgleich). Rokas et al. Nature 425, 798 (2003)

23. 8. Lecture WS 2003/04Bioinformatics III23 Reversal distance problem Extensive incongruence between trees derived from the 106 individual-gene data sets. Pairwise comparisons between 50% majority-rule consensus trees from 106 single-gene ML analyses of nucleotide data (black bars), MP analyses of nucleotide data (white bars), and MP analyses of amino acid data (grey bars) were categorized on the basis of the minimum number of taxa that need to be removed for two trees to reach congruence (x axis). For each of the analyses, the majority of pairwise comparisons require the removal of two or more taxa before congruence is attained. Rokas et al. Nature 425, 798 (2003)

24. 8. Lecture WS 2003/04Bioinformatics III24 What leads to incongruence? Many factors were checked that could lead to incongruence between single-gene phylogenies: - outgroup choice repeat all analyses without C. albicans - number of variable sitessignificantly correlated with - number of parsimony-informative sitesbootstrap values for some - gene sizebranches - rate of evolution - nucleotide composition - base compositional bias - genome location - gene ontology no parameters can systematically account for or predict the performance of single genes! } Rokas et al. Nature 425, 798 (2003)

25. 8. Lecture WS 2003/04Bioinformatics III25 Can incongruence be overcome? Although we do not know the cause(s) of incongruence between single-gene phylogenies, the critical question is how this incongruence between single trees might be overcome to arrive at the actual species tree. Can single gene trees be concatenated into one large data set? Rokas et al. Nature 425, 798 (2003)

26. 8. Lecture WS 2003/04Bioinformatics III26 Concatenation of single genes gives a single tree! Phylogenetic analyses of the concatenated data set composed of 106 genes yield maximum support for a single tree, irrespective of method and type of character evaluated. Numbers above branches indicate bootstrap values (ML on nucleotides/MP on nucleotides/MP on amino acids). All alternative topologies were rejected. This level of support for a single tree with 5 internal branches is unprecedented. This tree can now be referred to as species tree. Rokas et al. Nature 425, 798 (2003)

27. 8. Lecture WS 2003/04Bioinformatics III27 How much data is required? The concatanated data recovered a tree with maximum support on all branches, despite divergent levels of support for each branch among single-gene analyses.  At what size did the data set arrive at the species tree? Rokas et al. Nature 425, 798 (2003)

28. 8. Lecture WS 2003/04Bioinformatics III28 Convergence on single tree A minimum of 20 genes is required to recover >95% bootstrap values for each branch of the species tree. a, b, The bootstrap values for branches 3 (a) and 5 (b) were constructed from the concatenation of randomly re-sampled orthologous nucleotides (left) or random subsets of genes (right). The species tree is recovered with robust support (>95% bootstrap values in all branches at 95% confidence interval) by analyses of a minimum of 20 concatenated genes. All analyses were performed using MP. branch 3 branch 5 Rokas et al. Nature 425, 798 (2003)

29. 8. Lecture WS 2003/04Bioinformatics III29 Independent evolution? It has been suggested that nucleotides within a given gene do not evolve independently. Re-sample subset of orthologous nucleotides from the total data set. Only 3000 randomly chosen nucleotide positions (corresponding to less than three concatenated genes) are sufficient to generate single tree with > 95% confidence. This indicates that nucleotides in genes have not evolved independently (because when using complete genes more than 20 genes are necessary to generate single tree). Rokas et al. Nature 425, 798 (2003)

30. 8. Lecture WS 2003/04Bioinformatics III30 Implications for resolution of phylogenies Unreliability of single-gene data sets stems from the fact that each gene is shaped by a unique set of functional constraints through evolution. Phylogenetic algorithms are sensitive to such constraints. Such problems can be avoided with genome-wide sampling of independently evolving genes. In other cases the amount of sequence information needed to resolve specific relationships will be dependent on the particular phylogenetic history under examination. Branches depicting speciation events separated by long time intervals may be resolved with a smaller amount of data, and those depicting speciation events separated by shorter invtervals may be much harder to resolve. Rokas et al. Nature 425, 798 (2003)

31. 8. Lecture WS 2003/04Bioinformatics III31 Summary Robust strategies exist for phylogenies built on single-gene comparisons (maximum parsimony, distance, maximum likelihood). Problem of incongruence of phylogenies derived from individual genes. Can be resolved by integrative analysis of multiple (here > 20) genes. It is desirable to combine results from phylogenies constructed from local sequence information with trees constructed from genome rearrangement. The power of genome rearrangement studies is the construction of ancestral genomes. Then one can derive the speed of evolution at different times, disect mutation biases at different times from the influence of genomic context... and possibly derive the driving forces of biological evolution. This lecture rounds up the first block of the Bioinformatics III course on genome structure, rearrangements etc. Next block until Christmas: gene finding, SNPs, functional genomics