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1 Bioinformatics course Phylogeny and Comparative genomics 10/23/13 1
2 Contents-phylogeny Introduction-biology, life classificationtaxonomy Phylogenetic-tree of life, tree representation Why study phylogeny? Types of trees phylogenetic tree building methods 10/23/13 2
3 Contents-Comparative genomics Comparative genomics -concepts Why study comparative genomics? What can be compared? What do we learn from comparison? 10/23/13 3
4 Biology The Study of Life Life arose more than 3.5 billion years ago First organisms (living things) were single celled Only life on earth for millions of years Organisms changed over time (evolved) 10/23/13 4
5 New organisms arose from older kinds Today there are millions of species They inhabit almost every region of Earth today 10/23/13 5
6 Themes of Biology Cell structure and function Stability and homeostasis Reproduction and inheritance Evolution Interdependence of organisms Matter, energy, and organization 10/23/13 6
7 Cell Structure and Function Cell basic unit of life All organisms are made of and develop from cells Some composed of only a single cell (unicellular) which is usually identical to parent 10/23/13 7
8 Cells contain specialized structures (organelles) that carry out the cell s life processes Many different kinds of cells exist All cells surrounded by a plasma membrane Contain a set of instructions called DNA (genetic information) 10/23/13 8
9 Genotype and Phenotype 10/23/13 9
10 Biological classification 10/23/13 10
11 Genome size Completion Organism date Size Description phage phix ,368 bp 1st viral genome human mtdna ,571 bp 1st organelle genome lambda phage ,502 bp important virus model HIV ,193 bp AIDS retrovirus H. influenzae ,830 Kb 1st bacterial genome M. genitalium Kb smallest bacterial genome S. cerevisiae Mb 1st eukaryotic genome E. coli K Mb bacterial model organism C. trachomatis ,042 Kb internal parasite of eukaryotes D. melanogaster Mb fruit fly, model insect A. thaliana Mb thale cress, model plant H. sapiens ,000 Mb human SARS ,751 bp coronavirus 10/23/13 11
12 Total number of eukaryotic species 10/23/13 12
13 Phylogenetics Phylogenetics is the study of evolutionary relatedness among various groups of organisms (e.g., species, populations) The results of phylogene1c analysis are usually presented as a collec1on of nodes and branches. That is, a tree In such tree, taxa that are closely related in an evolu1onary sense appear close to each other, and taxa that are distantly related are in different (far) branches of the trees 10/23/13 13
14 Why study phylogeny? Find evolutionary ties between the organism, that is analyze the changes occurring in different organisms during evolution Find relation between ancestral sequence and its descendants Estimate time of divergence between the group of organisms sharing a common ancestor. 10/23/13 14
15 A Long, Long Time Ago Charles Darwin ( ) was the first to produce an evolutionary tree of life. He was very cautious about the possibility of reconstructing the history of life. On the Origin of Species (1859) 2 10/23/13 15
16 TREE OF LIFE 10/23/13 16
17 Phylogenenetic trees Aardvark Bison Chimp Dog Elephant Leafs - current day species Nodes - hypothetical most recent common ancestors Edges length - time from one speciation to the External nodes: things under comparison; operational taxonomic units (OTUs) 10/23/13 17
18 Rooted and unrooted trees unrooted rooted 10/23/13 18
19 Common players of evolution Relatedness- homology, orthology, parlogy Mutations SNPS Duplications Translocations and may more.. 10/23/13 19
20 Common players of evolution 10/23/13 20
21 Mutations kinds 10/23/13 21
22 Denovo mutations 10/23/13 22
23 Single nucleotide polymorphisms (snps) 10/23/13 23
24 Gene vs species trees Species tree Gene tree 10/23/13 24
25 Evolution-Related Concepts Homologs: Genes sharing a common ancestor and generally retain same function Orthologs: Genes (homologs) in different species derived from a single ancestral gene in the last common ancestor (LCA) (arise from speciation) Paralogs: Homologs in same species related via duplication Duplication before speciation (ancient duplication) Out-paralogs; may not have the same function Duplication after speciation (recent duplication) In-paralogs; likely to have the same function 10/23/13 25
26 Gene vs species trees Gene tree The genealogy of taxa, individuals of a population, etc Nodes represent speciation or other taxonomic events. Contains sequences from only orthologous genes. 10/23/13 Species tree Evolutionary history of the genes Nodes provide evidence for gene duplication events, as well as speciation events. Contains sequences from different homologs. Subsequent analyses should cluster orthologs, demonstrating its evolutionary history. 26
27 Molecular clock All the mutations occur in the same rate in all the tree branches The rate of mutations is same for all the positions along the sequences The molecular clock hypothesis is most suitable for closely related species. 10/23/13 27
28 Molecular clock 10/23/13 28
29 Tree building Distance methods UPGMA Neighbor-joining For distances use matrix like PAM or BLOSUM parsimony-based methods -Maximum parsimony character-based methods -maximum likelihood - Bayesian inference 10/23/13 29
30 What do edge lengths represent? In some trees edges represent time, in which case all modern sequences should be the same distance from the root. Sometimes edge lengths represent the product µ t of the rate of change µ and time t in which case different tips can be different distances from the root provided that the rate has changed across the tree. Cat Rat Dog 2 4 Cow 10/23/13 30
31 Distance matrices There are many ways of building phylogenetic trees, one family of methods uses a distance matrix as a starting point. A distance matrix is a table that indicates pairwise dissimilarity, for instance... Cat Dog Rat Cow Cat Dog Rat Cow A B C D B C D E /23/13 31
32 Where do we get distances from? Distances can be derived from Multiple Sequence Alignments (MSAs). The most basic distance is just a count of the number of sites which differ between two sequences divided by the sequence length. These are sometimes known as p- distances. Cat Dog Rat Cow ATTTGCGGTA ATCTGCGATA ATTGCCGTTT TTCGCTGTTT Cat Dog Rat Cow Cat Dog Rat Cow /23/13 32
33 Properties of distances d(x,x) = 0 d(x,y) = d(y,x) d(x,y) + d(y,z) >= d(x,z) (the triangle inequality) The distances used in phylogenetics always have the first two properties but sometimes not the third. 10/23/13 33
34 I want to build a tree - will any old distances do? Not all distances will be suitable for building trees. Tree-building methods do not discriminate, they will return a tree regardless of whether you give them roadmap distances or distances based on a sequence alignment. Some distances are perfectly tree-like. 10/23/13 34
35 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 35
36 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 36
37 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 37
38 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 38
39 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 39
40 Perfectly tree-like distances Dog 3 Cat Dog Rat Cat Rat Rat Cow Dog Cow 10/23/13 40
41 The 4-Point Condition Distances that fit exactly on a tree can be characterised by a condition on any quartet i, j, k, l (i.e. it must hold true for any 4 taxa). We write d(x,y) for the distance between x and y. Given 4 taxa i, j, k, l, of the 3 sums d(i,j) + d(k,l) d(i,k) + d(j,l) d(i,l) + d(j,k) The largest two are equal. Distances with this property are called additive, because the weights on the paths along the tree add up to the values in the distance matrix. 10/23/13 41
42 Why is this true of tree-like distances? i k i k i k j l j l j l d(i,j)+d(k,l) d(i,k)+d(j,l) d(i,l)+d(j,k) < = 10/23/13 42
43 Clock-like distances An even stricter condition on distances is that they fit on a clock-like tree. Distances with this property are called ultrametric. time d(i,k) = d(j,k) > d(i,j) i j k 10/23/13 43
44 Where do we get distances from? Distances can be derived from Multiple Sequence Alignments (MSAs). The most basic distance is just a count of the number of sites which differ between two sequences divided by the sequence length. These are sometimes known as p- distances. Cat Dog Rat Cow ATTTGCGGTA ATCTGCGATA ATTGCCGTTT TTCGCTGTTT Cat Dog Rat Cow Cat Dog Rat Cow /23/13 44
45 Tree building - UPGMA UPGMA works by progressively clustering the most similar taxa until all the taxa form a rooted clock-like tree. 1. Find the smallest entry in the distance matrix, say d(x,y). 2. Form a new internal node, z, that is a parent to x and y and set the edge lengths from z to x and z to y to half d(x,y). 3. Update the distance matrix by setting the distances from the new node z to all the other taxa to be the average distance between groups x and y. REPEAT until all groups have been joined. 10/23/13 45
46 What precisely is meant by the average distance? If we a joining two groups i and j that already have n i and n j members we update the distances using D n = ( i ) D + ( j ) ( i, j), k i, k n + n n + n i j i j n D j, k 10/23/13 46
47 F Step 1 Find the smallest entry in the distance matrix d(i,j) A B C D E F A - B 2 - C D E F G Step 2 - Cluster taxa A and B, form a new internal node I Calculate the lengths of the new edges d(a,i)=d(b,i)=1/2 d(a,b)=1 G A B C A 1 I E D E F 10/23/13 47 G 1 B C D Step 3 Update the distance matrix d(c,i) = ½(d(A,C) + d(b,c)) = 4 etc...
48 Step 1 Find the smallest entry in the distance matrix d(i,j) I (A+B) C D E F I (A+B) - C 4 - D E F G Step 2 - Cluster taxa C and D, form a new internal node II Calculate the lengths of the new edges d(c,ii)=d(d,ii)=1/2 d(c,d)=1 A 1 I B 1 C D E A I 1 B 1 C 1 D 1 II E Step 3 Update the distance matrix d(i,ii)=1/2(d(i,c)+d(i,d)) = 4 d(e,ii) = ½(d(E,C) + d(e,d)) = 7 etc... F 10/23/13 G F 48 G
49 And so on... F G E A B D C A B I G C D E F A B I G C D II E F A B C D I III G II E F A B C D F E G A B C D F E A B C D F III I IV II V 3.4 VI 3.8 I III II IV V G I III II IV G E...until we have a rooted tree. 10/23/13 49 But, is it the right tree?
50 UPGMA is not consistent for additive distances d(i,j) A B C D E F A - B 2 - C D E F G C D A B C D F E The tree that matches the distances is not recovered by UPGMA. G A B E = 1 I III 0.5 II IV V F 10/23/13 50 G 0.4 VI
51 Inconsistency When a method is given perfect data but still gets the wrong tree it is said to be inconsistent. UPGMA is inconsistent for data that isn t ultrametric (clock-like). Next we ll look at a method that is consistent for any additive data. 10/23/13 51
52 Neighbor-joining (NJ) NJ works by progressively clustering taxa until all the taxa form an unrooted tree. 1. Rather than using the distance matrix directly to determine which taxa should be clustered at each stage, NJ uses the S matrix where S(i,j) = (N-2)d(i,j) - R(i) - R(j) N is the number of taxa. R(i) is the sum of the ith row in the distance matrix. R(j) is the sum of the jth row in the distance matrix. 2. Find the smallest entry in the S matrix, say S(x,y). 10/23/13 52
53 3. Form a new internal node, z, that is a parent to x and y and calculate the edge lengths from z to x and z to y. d(x,z) = 1/(2(N-2))[(N-2)d(x,y) + R(x) R(y)] d(y,z) = d(x,y) d(x,z) 4. Update the distance matrix d(w,z) = ½ (d(x,w) + d(y,w) d(x,y)) REPEAT until only two things are left to be joined. 10/23/13 53
54 NJ Example D= Cat Dog Rat Dog 3 Rat 4 5 Cow Step 1 S= Cat Dog Rat Dog -22 Rat Cow R(cat) = 13 R(dog) = 15 R(rat) = 15 R(cow) = 19 e.g. S(cat,dog) = (4-2)x = -22 S(cat,rat) = (4-2)x = /23/13 54
55 NJ Example D= Cat Dog Rat Dog 3 Rat 4 5 Cow S= Cat Dog Rat Step 1 Dog -22 Step 2 Rat Cow Cat Rat Step 3 d(cat,z) = ¼[2d(cat,dog) + R(cat) R(dog)] = ¼ [ ] = 1 d(dog,z) = 3-1 = 2 Dog Cow 10/23/13 55 z
56 Gene expression and evolution 10/23/13 56
57 Evolution of vocal learning in birds 10/23/13 57
58 Did the Florida Dentist infect his patients with HIV? Phylogenetic tree of HIV sequences from the DENTIST, his Patients, & Local HIV-infected People: DENTIST Patient C Patient A Patient G Patient B Patient E Patient A DENTIST Local control 2 Local control 3 Patient F Local control 9 Local control 35 Local control 3 Patient D Yes: The HIV sequences from these patients fall within the clade of HIV sequences found in the dentist. No No From Ou et al., Science. 1992; 256: /23/13 58
59 Convergent evolution/ homoplasty 10/23/13 59
60 Ancestral character or pleisiomorphic state 10/23/13 60
61 Molecular Phylogeny Major Softwares Software/Package Description URL PHYLIP PAUP* includes programs to carry out parsimony, distance matrix methods, maximum likelihood, evolution.genetics.washington.edu/phylip.html and other methods on a variety of types of data, including DNA and RNA sequences, protein sequences, restriction sites, 0/1 discrete characters data, gene frequencies, continuous characters and distance matrices. originally including parsimony program, it has become much broader with the inclusion paup.csit.fsu.edu/ of more methods. It includes parsimony, distance matrix, invariants, and maximum likelihood methods and many indices and statistical tests. PAML a package of programs for the maximum likelihood analysis of nucleotide or protein abacus.gene.ucl.ac.uk/software/paml.html sequences, including codon-based methods that take into account both amino acids and nucleotides. The programs can estimate phylogenetic trees by maximum likelihood and Bayesian Markov Chain Monte Carlo methods. MrBayes Tree-Puzzle a program for Bayesian inference of phylogenies from nucleic acid or protein sequences. morphbank.ebc.uu.se/mrbayes/ It assumes a prior distribution of tree topologies and uses Markov Chain Monte Carlo (MCMC) methods to search tree space and infer the posterior distribution of topologies a program for maximum likelihood analysis for nucleotide and amino acid alignments. TREE-PUZZLE infers phylogenies by "quartet puzzling", a method that applies maximum likelihood tree reconstruction to all possible quartets of taxa and subsequently tries to combine most of the four-taxa maximum likelihood trees to construct an overall maximum likelihood tree. A consensus tree generated from the quartet puzzling trees shows nodes that are well supported. 10/23/13 61
62 Comparative genomics Compara1ve genomics studies differences between genome sequences pin- poin1ng changes over 1me. Comparison of the number/type changes against the background neutral expected changes provides a beher understanding of the forces that shaped genomes and traits. Insights into evolu1on 10/23/13 62
63 Defining comparative genomics The combination of genomics data and comparative evolutionary biology to address questions of genome structure, evolution and function 10/23/13 63
64 What is Comparative Genomics 10/23/13 64
65 Why comparative genomics? To understand the genomic basis of the present Differences in lifestyle pathogen vs. nonpathogenic obligate vs. free-living Host specificity In the case of emerging pathogens: this understanding should help us in fighting disease (drug discovery, vaccines) To understand the past How organisms evolved to be what they are now 10/23/13 65
66 What can we learn from cross species comparison? Helps in gene predictions Genome conservation transfer knowledge gained from model organisms to non-model organisms Genome variation Understand how genomes change over time to identify evolutionary process and constraints Detecting functional elements Identifying coding and non coding sequences 10/23/13 66
67 What to compare? What is the common set of proteins? What sequences show a signature of purifying selection and are likely functional? What sequence features are unique to individual species? 10/23/13 67
68 Synteny Organism A 1a 2a 3a 4a 5a 6a Block of synteny 7b 2b 3b 4b 8b 9b Organism B Refers to regions of two genomes that show considerable similarity in terms of sequence and conservation of the order of genes likely to be related by common descent 10/23/13 68
69 Conserva1on highlights exons Regulatory Element? Novel exon? 10/23/13 69
70 Sequence conserva1on doesn t imply func1on conserva1on Despite conserva.on of binding preferences and binding sites only a small propor.on of TF binding events is conserved across species 10/23/13 Odom D. et al (2007) 70 Schmidt D. et al (2010)
71 Lessons from comparative genomics Changes of protein coding repertoires and contributions to phenotypic differences same different contraction expansion Demuth J.P. et al, (2006) 10/23/13 71
72 Tools for comparative genomics Tool name UCSC genome browser Ensembl Mummer Blat BLAST Genscan Purpose Conserved regions using tracks contains information of several genomes whole genome sequence alignment Blast like sequence alignment tool (large genomes) alignment tools for smaller sequences Gene prediction tool 10/23/13 72
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