Phylogeny — A brief introduction in 4 hours

Phylogeny - A brief introduction in 4 hours -

Outline • • • Introduction Practical approach Evolutionary models Distance-based methods / TP 5_1 Databases and software Sequence-based methods / TP 5_2

What is phylogeny?

Phylogeny is the evolutionary history and relationship of species.

Why is phylogeny of interest in a proteomics course?

What data types can be used to infer phylogenies? • • Morphological characters Physiological characters Gene order (e. g. in mitochondria) Sequence data – Nucleotide sequences – Amino acid sequences • Mixed characters • ….

What is a phylogenetic tree? • A phylogenetic tree is a model about the evolutionary relationship between species (OTUs) based on homologous characters • But not all trees are phylogenetic trees – Dendrogram = general term for a branching diagram – Cladogram: branching diagram without branch length estimates – Phylogenetic tree or Phylogram: branching diagram with branch length estimates

What is a phylogenetic tree? • Rooted or unrooted • bifurcating or multifurcating (solved or unsolved)

Gene duplication • Prokaryots: at least 50% • Eukaryots: >90%

After gene duplication • Coexistence (normally only for a short while) • Mostly, only one copy is retained – becomes nonfunctional (non-functionalization), – becomes a pseudogene (pseudogenization) – is lost • Both copies are retained – Distinct expression pattern – Distinct subcellular location (rare) – One copy keeps the original function, the other copy acquires a new function (neofunctionalization) – Deleterious mutations in both entries (subfunctionalization)

Relationships within homologs Frog gene A Human gene A Gene duplication Orthologs Mouse gene A Paralogs Mouse gene B Ancestral gene Human gene B Frog gene B Drosophila gene AB Homologs Orthologs

Homologs … Homologs = Genes of common origin Orthologs = 1. Genes resulting from a speciation event, 2. Genes originating from an ancestral gene in the last common ancestor of the compared genomes Co-orthologs = Orthologs that have undergone lineage-specific gene duplications subsequent to a particular speciation event Paralogs = Genes resulting from gene duplication Inparalogs = Paralogs resulting from lineage-specific duplication(s) subsequent to a particular speciation event Outparalogs = Paralogs resulting from gene duplication(s) preceding a particular speciation event One-to-one (1: 1) orthologs = Orthologs with no (known) lineage-specific gene duplications subsequent to a particular speciation event One-to-many (1: n) orthologs: Orthologs of which at least one - and at most all but one - has undergone lineage-specific gene duplication subsequent to a particular speciation event Many-to-many (n: n) orthologs = Orthologs which have undergone lineagespecific gene duplications subsequent to a particular speciation event Xenologs = Orthologs derived by horizontal gene transfer from another lineage

Relationships between orthologs and paralogs Frog gene A Human gene A Gene duplication Mouse gene A Mouse gene B Ancestral gene Human gene B Frog gene B Drosophila gene AB Orthologs (Group 1) Inparalogs of Group 2 Outparalogs of Group 1 Co-orthologs of Drosophila gene AB Orthologs (Group 2)

Practical approach I Actin-related protein 2 (first 60 columns of the alignment) ARP 2_A ARP 2_B ARP 2_C ARP 2_D ARP 2_E MESAP---IVLDNGTGFVKVGYAKDNFPRFQFPSIVGRPILRAEEKTGNVQIKDVMVGDE MDSQGRKVIVVDNGTGFVKCGYAGTNFPAHIFPSMVGRPIVRSTQRVGNIEIKDLMVGEE MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGRPIIRSTTKVGNIEIKDLMVGDE MDSKGRNVIVCDNGTGFVKCGYAGSNFPTHIFPSMVGRPMIRAVNKIGDIEVKDLMVGDE *: * **** ***. **: : ****: : *: : **: * Species are: Caenorhabditis briggsae Drosophila melanogaster Homo sapiens Mus musculus Schizosaccharomyces pombe Can you build a dendrogram (tree) for the sequences of the alignment? Can you assign the species to the corresponding sequences of the alignment?

Phylogenetic analysis 1. 2. 3. 4. 5. Select Data Alignment Select a data model Select a substitution model Tree-building • • [Distance matrix] Tree-building 6. Tree evaluation

Select data • To be considered: – Input data must be homolog! – Number of character states – Content of phylogenetic information – Size of the dataset – Automated cluster data from large datasets – etc

Alignment • MSA methods – Clustal. W – muscle – MAFFT – Probcons – T-coffee –… • See previous course …

Data model = Characters selected for the analysis • To be considered: – Each character should be homolog! – Missing data (in some OTU) – Number of characters – etc

Evolutionary models Phylogenetic tree-building presumes particular evolutionary models The model used influences the outcome of the analysis and should be considered in the interpretation of the analysis results • Which aspects are to be considered? 1. 2. 3. 4. Frequencies of aa exchange Change of aa frequencies during evolution Between-site rate variation or Among-site substitution rate heterogenity Presence of invariable sites

Evolutionary models Notation, e. g. JTT + F + gamma (4 ) JTT + F + gamma (8 ) + I (under discussion) JTT + F + I It is not always the most complex model that produces the best result. The more complex the model, the more complex the explanation of the results.

Tree-building methods • Distance (matrix) methods 1. Calculate distances for all pairs of taxa based on the sequence alignment 2. Construct a phylogenetic tree based on a distance matrix • Character-based (Sequence) methods 1. Constructs a phylogenetic tree based on the sequence alignment

Step 1: Compute distances 1. Estimate the number of amino acid substitutions between sequence pairs p distance: ^p=nd/n p = proportion (p distance) nd= number of aa differences n = number of aa used

Step 1: Compute distances • Nonlinear relationship of p with t (time) • Estimation of aa substitutions – Poisson correction • PC distance – Gamma correction • Gamma distance

Step 2: Tree-building Common distance methods • Neighbor Joining (NJ) • UPGMA / WPGMA • Least Square (LS) • Minimal Evolution (ME)

Neighbor Joining (NJ) • Saitou, Nei (1987) • Principle – Clustering method – Simplified minimal evolution principle – Neighbors = taxa connected by a single node in an unrooted tree – Computational process: Star tree, followed by a successive joining of neighbors and the creation of new pairs of neighbors – Result: • A single final tree with branch length estimates • unrooted tree

Neighbor Joining (NJ) • Sum of branch lengths in the star tree • Calculate the sum of all branch lengths for all possible neighbors …

Neighbor Joining (NJ) • Calculate Length X-Y • Calculate again sum of all branch length

Neighbor Joining (NJ)

Neighbor Joining (NJ) • Advantage – Very efficient – Also for large datasets • Disadvantage – Does not examine all possible topologies

Bootstrap • • Used to test the robustness of a tree topology by Bradley Efron (1979) Felsenstein (1985) Principle: new MSA datasets are created by choosing randomly N columns from the original MSA; where N is the length of the original MSA • 100 -1000 replicates • Bootstrap support values: (75%), 95%, 98%

TP 5 - st 1 part, Exercises 1 -5 http: //education. expasy. org/m 07_phylo. html

Ortholog databases & phylogenetic databases Some databases providing orthologous groups and trees • • • COG/KOG HOGENOM Ensembl OMA browser Ortho. DB Ortho. MCL • • • Pfam PANDIT SYSTERS Tree. Base Tree of Life

Phylogenetic software Software packages • Freely available – – – Phylip Bio. NJ Phy. ML Tree Puzzle Mr. Bayes • Commercial – PAUP – MEGA

Phylogenetic servers • • • http: //www. phylogeny. fr/ http: //bioweb. pasteur. fr/seqanal/phylogeny/intro-uk. html http: //atgc. lirmm. fr/phyml/ http: //phylobench. vital-it. ch/raxml-bb/ http: //www. fbsc. ncifcrf. gov/app/htdocs/appdb/drawpage. php? ap pname=PAUP • http: //power. nhri. org. tw/power/home. htm

Sequence methods Most common: • Maximum Parsimony (MP) • Maximum Likelihood (ML) • Baysian Inference

Maximum Parsimony (MP) • • • Originally developed for morphological characters Henning, 1966 William of Ockham: the best hypothesis is the one that requires the smallest number of assumptions

Maximum Parsimony (MP) • – – – Principle: Estimate the minimum number of substitutions for a given topology Parsimony-informative sites (exclude invariable sites and singletons) Searching MP trees Exhaustive search Branch-and-bound (Hendy-Penny, 1982) • – • • Heuristic search – Good but time-consuming, if m>20 – Result tree might not be the most parsimonious tree Result • • • Multiple result trees are possible (strict consensus tree, majority-rule consensus tree) Most parsimonious tree vs true tree Unrooted result trees

Maximum Parsimony (MP) • Advantages – • Free from assumptions (model-free) Disadvantages – – Does not take into account homoplasy Long-branch attraction (LBA): creates wrong topologies, if the substitution rate varies extensively between lineages

Maximum Likelihood (ML) • • – Cavalli-Sforza, Edwards (1967), gene frequency data Felsenstein (1981), nucleotide sequences Kishino (1990), proteins Principle Maximizes the likelihood of observing the sequence data for a specific model of character state changes Likelihood of a site = Sum of probabilities of every possible reconstruction of ancestral states at the internal nodes Likelyhood of the tree = Product of the likelihoods for all sites (=sum of log likelihoods) Result = tree with the highest likelihood – – • • – Maximized to estimate branch lengths, not topologies Search strategies: rarely exhaustive, mostly heuristic • • • NNI (Nearest neighbor interchanges) TBR (Tree bisection-reconnection) SPR (Subtree pruning and regrafting)

Number of possible trees • Unrooted bifurcating trees: • Rooted bifurcating trees:

Number of possible trees Leaves Rooted Unrooted

Number of possible trees Leaves Unrooted Rooted 3 1 3 4 3 15 5 15 105 6 105 945 7 945 10395 8 10395 135135 9 135135 2027025 10 2027025 34459425

Maximum Likelihood (ML) • Methods: – – Pro. ML (Phylip) Phy. ML Rax. ML …

Tree evaluation 1. Topology 1. Comparison with species tree 2. Robustness, e. g. bootstrap 2. Branch lengths

TP 5 – nd 2 part, Exercise 6 http: //education. expasy. org/m 07_phylo. html