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Phylogenetic trees

Phylogenetic trees. Phylogeny is the inference of evolutionary relationships. Traditionally, phylogeny relied on the comparison of morphological features between organisms. Today, molecular sequence data are mainly used for phylogenetic analyses. One tree of life A sketch Darwin made

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Phylogenetic trees

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  1. Phylogenetic trees

  2. Phylogenyis the inference of evolutionary relationships. Traditionally, phylogeny relied on the comparison of morphological features between organisms. Today, molecular sequence data are mainly used for phylogenetic analyses. One tree of life A sketch Darwin made soon after returning from his voyage on HMS Beagle (1831–36) showed his thinking about the diversification of species from a single stock (see Figure, overleaf). This branching, extended by the concept of common descent,

  3. Haeckel (1879) Pace (2001)

  4. Human Chimpanzee Gorilla Orangutan Molecular phylogeny uses trees to depict evolutionary relationships among organisms. These trees are based upon DNA and protein sequence data Gorilla Chimpanzee Orangutan Human Pre-Molecular analysis: The great apes (chimpanzee, Gorilla & orangutan) Separate from the human Molecular analysis: Chimpanzee is related more closely to human than the gorilla

  5. What can we learn from phylogenetics tree?

  6. 1. Determine the closest relatives of one organism in which we are interested • Was the extinct quagga more like a zebra or a horse?

  7. Human Chimpanzee Gorilla Orangutan Which species are closest to Human? Gorilla Chimpanzee Orangutan Human

  8. 2. Help to find the relationship between the species and identify new species Example Metagenomics A new field in genomics aims the study the genomes recovered from environmental samples. A powerful tool to access the wealthy biodiversity of native environmental samples

  9. 106 cells/ ml seawater 107 virus particles/ ml seawater >99% uncultivated microbes

  10. From : “The Sorcerer II Global Ocean Sampling Expedition: Metagenomic Characterization of Viruses within Aquatic Microbial Samples” Williamson et al, PLOS ONE 2008

  11. 3. Discover a function of an unknown gene or protein RBP1_HS RBP2_pig Hypothetical protein RBP_RAT ALP_HS ALPEC_BV ALPA1_RAT ECBLC Hypothetical protein X Hypothetical protein

  12. Relationships can be represented byPhylogenetic Tree or Dendrogram F E D B A C

  13. Phylogenetic Tree Terminology • Graph composed ofnodes &branches • Each branch connects two adjacent nodes R F E D B A C

  14. Phylogenetic Tree Terminology Rooted tree Un-rooted tree Human Chicken Gorilla Chimp Gorilla Human Chimp Chicken

  15. Rooted vs. unrooted trees 3 1 2 3 1 2

  16. How can we build a tree with molecular data? -Trees based on DNA sequence (rRNA) -Trees based on Protein sequences atcgatcgtgatcgatcgtagcatcgatgcatcgtacg MWRCPYCGKRQWCMWG

  17. Questions: • Can DNA and proteins from the same gene produce different trees ? • Can different genes have different evolutionary history ? • Can different regions of the same gene produce different trees ?

  18. Approach 1 - Distance methods • Two steps : • Compute a distance between any two sequences from the MSA. • Find the tree that agrees most with the distance table. • Algorithms : -Neighbor joining Approach 2 - State methods • Algorithms: • Maximum parsimony (MP) • Maximum likelihood (ML)

  19. a b d c Basic algorithm forconstructing a rooted tree (UPGMA) Assumption: Divergence of sequences is assumed to occur at a constant rate  Distance to root is equal

  20. a b d c Basic Algorithm of Neighbor Joining (Unrooted tree) starting from a start diagram Distance matrix Initial star diagram 20

  21. a b d c Selection step Choose the nodes with the shortest distance and fuse them. 21

  22. a a e c,b d Next Step Then recalculate the distance between the rest of the remaining sequences (a and d) to the new node (e) and remove the fused nodes from the table. D (EA) = (D(AC)+ D(AB)-D(CB))/2 D (ED) = (D(DC)+ D(DB)-D(CB))/2

  23. Next Step a c Dce e d Dde b In order to get a tree, un-fuse c and b by calculating their distance to the new node (e) !!!The distances Dce and Dde are calculated independently (the formula will be given in the tirgul)

  24. a Next… We want to fuse the next closest nodes c Dce a,d e f Dde b

  25. Finally We need to calculate the distance between e and f c a Daf e f Dce Dbf Dde b d D (EF) = (D(EA)+ D(ED)-D(AD))/2

  26. a c Dce a,d e e f c,b d Dde c a b Daf e f Dce Dbf Dde b 1 2 3 d 26

  27. IMPORTANT !!! • Usually we don’t assume a constant mutation rate and in order to choose the nodes to fuse we have to calculate the relative distance matrix (Mij) representing the relative distance of each node to all other nodes

  28. EXAMPLE Original distance Matrix Relative Distance Matrix (Mij) The Mij Table is used only to choose the closest pairs not for calculating the distances

  29. Neighbor Joining (NJ) • Reconstructs an unrooted tree • Calculates branch lengths Based on pairwise distances • In each stage, the two nearest nodes of the tree are chosen and defined as neighbors in our tree. This is done recursively until all of the nodes are paired together.

  30. Advantages and disadvantages of the neighbor-joining method • Advantages • -It is fast and thus suited for large datasets • -Permits lineages with largely different branch lengths • Disadvantages • - Sequence information is reduced • - Gives only one possible tree

  31. Problems with phylogenetic trees - Using different regions from a same alignment may produce different trees.

  32. Problems with phylogenetic trees

  33. Problems with phylogenetic trees Bacillus Bacillus Burkholderias Aeromonas Aeromonas Pseudomonas Pseudomonas Burkholderias Lechevaliera Lechevaliera E.coli E.coli Salmonella Salmonella Bacillus Pseudomonas Pseudomonas Aeromonas Burkholderias Burkholderias Aeromonas Bacillus Lechevaliera Lechevaliera E.coli E.coli Salmonella Salmonella

  34. Problems with phylogenetic trees • It is wrong to produce a tree based on distance values of the whole alignment • What to do?: use bootstrap

  35. Bootstrapping • We create new data sets by sampling N positions with replacement. • We generate 100 - 1000 such pseudo-data sets. • For each such data set we reconstruct a tree, using the same method. • We note the agreement between the tree reconstructed from the pseudo-data set to the original tree. • Note: we do not change the number of sequences !

  36. Bootstrapped tree Less reliable Branch Highly reliable branch

  37. Tools for tree reconstruction • CLUSTALX (NJ method) • Phylip -PHYLogeny Inference Package • includes parsimony, distance matrix, and likelihood methods, including bootstrapping. • Phyml (maximum likelihood method) • MEGA (Molecular Evolution Genomic Analysis) • More phylogeny programs

  38. 362

  39. http://www.phylogeny.fr

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