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Phylogenetics

Phylogenetics. 3/2/2018. Acknowledgements. Much of the content of this lecture is from:. Yang (2012) – Molecular phylogenetics principles and practices. What is phylogenetics?. Study of evolutionary history among groups of organisms. Phylogenetics and Bioinformatics.

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Phylogenetics

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  1. Phylogenetics 3/2/2018

  2. Acknowledgements Much of the content of this lecture is from: • Yang (2012) – Molecular phylogenetics principles and practices

  3. What is phylogenetics? Study of evolutionary history among groups of organisms

  4. Phylogenetics and Bioinformatics • A foundational topic in bioinformatics • Subject of research in 1980s and 1990s as DNA sequences became more available • Renewed interest now with NGS and the explosion of sequencing data • Phylogenetics itself has been around for hundreds of years…

  5. Taxonomy • History of phylogenetics is rooted in taxonomy • Taxonomy: defining and naming organisms based on shared characteristics • A basic taxonomy has likely always been in place

  6. Pre-Linnaean Taxonomy • Egyptian paintings from ~1500 BC depict plants with medicinal properties • Aristotle (384-322 BC) classified animals based on attributes (number of legs, laying eggs, warm-bodied, etc.) • Bhagavata Purana (Hindu texts from 500-1000 CE) explicitly defines 6 sub-types of plants (large trees with and w/o fruits, small plants, bushes) • Andrea Cesalpino (1519-1603), the “first taxonomist”, formally describes ~1500 plant species in 1583

  7. Carl Linnaeus (1707-1778) • Swedish botanist, zoologist, physician • Systema Naturae(1735) established three “kingdoms” – animal, vegetable, and mineral • We still use hierarchy described in the 10th edition (1758) – kingdoms, classes, orders, genera, species • Popularized use of binomial nomenclature (eg. Homo sapiens) • 1st edition: 11 pages - whales are fish • 10th edition: 1,300 pages - whales are mammals

  8. From taxonomy to phylogenetics • Phylogenetics is taxonomy but applied to theory of evolution • Related species in these classifications have a common ancestor • Charles Darwin used a tree to illustrate evolution, ancestors in On the Origin of Species (1859) • Darwin’s notes from 1837 show his first tree sketch

  9. Molecular Phylogenetics • Today, bioinformatics uses sequencing data to study phylogenetics (instead of observable traits) • Molecular phylogenetics leverages similarities and differences between DNA sequences to gain information on evolutionary relationships

  10. Molecular Phylogenetics GOAL: examine and visualize relationships between a group of species or DNA elements within a species (gene paralogs) using DNA sequences themselves

  11. Applications of Phylogenetics • Origin and spread of a viral infection • Genealogical relationship between cells during cancer development • Origin and relationship between paralogs of a gene • Migration patterns of a species • Evolution of language • Classification of metagenomics samples • Annotation of newly sequenced genomes • Reconstruction of ancestral genomes

  12. This Lecture • Basic phylogenetic tree concepts • Strategies and methodologies for tree reconstruction • Assessment of phylogenetic methods • Some commonly used phylogenetics tools

  13. Phylogenetic Trees

  14. Phylogenetic Tree Terminology clade Taxon (OTU) Branch (lineage) Internal Node (shared ancestor) Terminal node Root outgroup Branch Length (time)

  15. The Phylogenetic Tree as a Model • Nodes 1, 2, and 3 are separated by 2 speciation events at T0 and T1 • Branch lengths (b) are units of substitution and measure evolution over time • If substitution rate is constant: b0 + b1 = b0 + b2 = b3

  16. Rooted Tree vs. Unrooted Tree • If each branch length has independent evolutionary rate, unable to identify root • Common strategy is to include an outgroup to root the tree

  17. Phylogenetic Tree Reconstruction • For molecular phylogenetics, you start with some group of DNA, RNA, or protein sequences • You first need to perform a multiple sequence alignment (MSA) • Tree reconstruction based on MSA can either be done using distance-based or character-based methods

  18. Multiple Sequence Alignment

  19. DNA Sequence Alignment (Redux) Sequence 1 ATACACAGTAGGAGATACCAGTAAGGGAGGGGG Sequence 2 ATACCATAAGCGAG Match Mismatch ATACACAGTAGGAGATACCAGTAAGGGAGGGGG --------------ATACCA-TAAGCGAG---- Alignment 1 Gap ATACACAGTAGGAGATACCAGTAAGGGAGGGGG ATAC-CA--------------TAAGCGAG---- Alignment 2 Alignment 3 ATACACAGTAGGAGATACCAGTAAGGGAGGGGG ATAC-CA-TA--AG---C--G--AG--------

  20. Scoring/Substitution Matrices • Given alignment, how “good” is it? • Higher score = better alignment • Implicitly represent evolutionary patterns ATACCAGTAAGGGAG ATACCA-TAAGAGAG Score = 22 ATACCAGTAAGG-GAG ATACCA-TAAG-AGAG Score = 19 ATACCA-GTAAGGGAG A-TACCATAAGAGAG- Score = -20

  21. Multiple Sequence Alignment Like pairwise alignment, but with N sequences

  22. Obtaining a Multiple Sequence Alignment • Example using ClustalW2 • Input: group of FASTA sequences • Output: Clustal format alignment * = identical : = conserved . = semi-conserved

  23. From MSA to Phylogenetic Tree

  24. Phylogenetic Tree Reconstruction

  25. Methods for Tree Reconstruction Character-based methods • Maximum parsimony • Maximum likelihood • Bayesian methods Distance-based methods • UPGMA/WPGMA • Neighbor joining • Least squares

  26. Distance-based methods • Use your obtained multiple sequence alignment to compute distances between sequences • Pairwise distances are measured using a substitution matrix (as with scoring alignments) • Pairwise distances between all sequences in your MSA generate a distance matrix

  27. Computing distances • Different substitution models exist • Example: JC69 (Jukes-Cantor) • Model scores every substitution equally • Identical nucleotides at given position scored 0

  28. Different Substitution models • Size of circles = relative proportion of given nucleotide • Thickness of arrow = relative substitution rate

  29. Distance Matrix Example • Given 5 arbitrary sequences – A, B, C, D, and E • Computing pairwise distances using a substitution model generates a 5x5 matrix • Distance matrix can be used to construct a tree

  30. WPGMA Example • Weighted Pair Group Method with Arithmetic Mean • Start with initial pairwise distance matrix A B C • A and B are closest • Join them and compute new distances D E

  31. WPGMA Example • A and B are merged into AB • Distance from AB to C, D, and E are computed A B C • D and E are now closest • Join them and compute new distances D E

  32. WPGMA Example • D and E are merged to DE • Distance from DE to C and AB are computed A B C • C and DE are now closest • Join them and compute new distances D E

  33. WPGMA Example • C and DE are merged into CDE • Distance from CDE to AB is computed A B C • Join final 2 clusters D E

  34. WPGMA Example A B C D • Distances between clusters used to plot rooted tree • Assumes constant evolutionary rate E

  35. Neighbor joining • Initialized with a star network • Iteratively joins nearest taxa, assigning internal nodes • Results in an unrooted tree • Does not assume lineages evolve at same rate

  36. Distance-based methods Strengths: • SPEED and computational efficiency • NJ is good for large data sets with low levels of sequence divergence Weaknesses: • Loss of information (condensing MSA) • No attempts to define internal ancestral nodes

  37. Character-based methods Maximum parsimony • Attempts to find most parsimonious tree • This is the tree that relates the sequences with the least number of mutations Mutations only

  38. Maximum Parsimony Example

  39. Maximum Parsimony Example

  40. Maximum Parsimony Example

  41. Maximum Parsimony Example

  42. Finding the most parsimonious tree • Not exactly an easy task • The number of unrooted trees with n taxa is:

  43. Maximum Likelihood • Likelihood: conditional probability of observing the sequences given a model of evolution and a tree • We’re trying to find the tree and branch lengths that maximize the likelihood function

  44. Maximum Likelihood • Assuming independent substitution rates:

  45. Maximum Likelihood • Likelihoods typically calculated from leaves to root (Felsenstein’s pruning algorithm) • In practice, impossible to visit every tree

  46. Assessing trees and reconstruction methods

  47. Assessing tree topology • Most common way to assess confidence in a tree topology for both distance-based and character-based analyses is the bootstrap analysis • Bootstrapping – number of sites in the MSA are resampled with replacement as many times as the sequence length, generating a pseudo-sample • For each clade in tree, bootstrap support value is proportion of trees that include that clade

  48. Bootstrap Analysis

  49. Assessing reconstruction methods • Consistency – parameter values converge with increasing data • Efficiency – probability of recovering correct tree given the number of sites in comparison (estimated by simulation) • Robustness – gives correct answers even when assumptions are violated or relaxed • Speed / computational efficiency

  50. Phylogenetics Workflow

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