1. Sequence Alignment and Phylogenetic Analysis

2. Evolution

3. Sequence Alignment -AGGCTATCACCTGACCTCCAGGCCGA--TGCCC--- TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC Definition Given two strings x = x 1 x 2...x M, y = y 1 y 2 …y N, an alignment is an assignment of gaps to positions 0,…, N in x, and 0,…, N in y, so as to line up each letter in one sequence with either a letter, or a gap in the other sequence AGGCTATCACCTGACCTCCAGGCCGATGCCC TAGCTATCACGACCGCGGTCGATTTGCCCGAC

4. 4 Example HEAGAWGHEE 0-8-16-24-32-40-48-56-64-72-80 P-8-2-9-17-25-33-42-49-57-65-73 A-16 W-24 H-32 E-40 A-48 E-56 AEGHW A50-2-3 E6-30 H-20 10-3 P -2 -4 W-3 15

5. 5 HEAGAWGHEE 0-8-16-24-32-40-48-56-64-72-80 P-8-2-9-17-25-33-42-49-57-65-73 A-16-10-3-4-12-20-28-36-44-52-60 W-24-18-11-6-7-15-5-13-21-29-37 H-32-14-18-13-8-9-13-7-3-11-19 E-40-22-8-16 -9-12-15-73-5 A-48-30-16-3-11 -12 -15-52 E-56-38-24-11-6-12-14-15-12-91 AEGHW A50-2-3 E6-30 H-20 10-3 P -2 -4 W-3 15

6. The Blosum50 Scoring Matrix

7. Multiple Alignment

8. Example

9. ClustalW Popular multiple alignment tool today ‘W’ stands for ‘weighted’ (d ifferent parts of alignment are weighted differently). Three-step process 1.) Construct pairwise alignments 2.) Build Guide Tree 3.) Progressive Alignment guided by the tree

10. Step 1: Pairwise Alignment

12. Step 3: Progressive Alignment Start by aligning the two most similar sequences Following the guide tree, add in the next sequences, aligning to the existing alignment Insert gaps as necessary

14. Some Guidelines for Choosing the Right Sequences

15. Gathering Sequences with BLAST The most convenient way to select your sequences is to use a BLAST server Some BLAST servers are integrated with multiple-alignment methods: www.expasy.ch (protein only) srs.ebi.ac.uk (DNA/protein) npsa-pbil.ibcp.fr

16. Selecting a Method Many alternative methods exist for MSAs Most of them use the progressive algorithm They all are approximate methods None is guaranteed to deliver the best alignments All existing methods have pros and cons ClustalW is the most popular (21,000 citations) T-Coffee and ProbCons are more accurate but slower MUSCLE is very fast, ideal for very large datasets

17. ClustalW www.ebi.ac.uk/clustalw pir.georgetown.edu/pirwww/search/multia ln.shtml www.ddbj.nig.ac.jp/search/clustalw-e.html

19. Tcoffee TCOFFEE: www.tcoffee.org CORE: evaluate MSA MCOFFEE: run many and combine EXPRESSO: with structural information

21. Running Many Methods at Once MCOFFEE is a a meta-method It runs all the individual MSA methods It gathers all the produced MSAs It combines the MSAs into a single MSA MCOFFEE is more accurate than any individual method Its color output lets you estimate the reliability of your MSA MCOFFEE is available on www.tcoffee.org

22. Editing and Publishing Alignments

23. Alignments and Formats Many alternative formats exist for MSAs One format does not always have a clear advantage over another Changing formats is possible Annotation information can sometimes be lost in a format change Not all formats contain the same information The annotation may change Reformatting may cause the loss of annotation information

24. The Most Common Sequence Formats

25. Interleaved and Non-interleaved The MSF Format Interleaved The FASTA Format Non-interleaved

26. Choosing Your Format When choosing a format, ask yourself four questions: Is it supported by the programs I need to use ? Can my collaborators use it? Can it support all of my annotation ? Is it easy to read and manipulate ?

27. Converting Formats Don’t re-compute your MSA if it is not in the right format Convert your file using one of the online conversion tools The 3 most popular reformatting utilities: Fmtseq The most complete RESDSEQ Very popular and robust SeqCheck Can clean FASTA sequences

28. An Alignment CLUSTAL 2.1 multiple sequence alignment sp|P02620|PRVB_MERME ---------------------------------------------AFAGI 5 sp|P02622|PRVB_GADCA ---------------------------------------------AFKGI 5 sp|P02619|PRVB_ESOLU ---------------------------------------------SFAGL 5 sp|Q91482|PRVB1_SALSA --------------------------------------------MACAHL 6 sp|P43305|PRVU_CHICK --------------------------------------------MSLTDI 6 sp|P20472|PRVA_HUMAN --------------------------------------------MSMTDL 6 sp|P80079|PRVA_FELCA --------------------------------------------MSMTDL 6 sp|P02627|PRVA_RANES ---------------------------------------------PMTDL 5 sp|P02626|PRVA_AMPME ---------------------------------------------SMTDV 5 sp|P02586|TNNC2_RABIT MTDQQAEARSYLSEEMIAEFKAAFDMFDADGGGDISVKELGTVMRMLGQT 50 sp|P02620|PRVB_MERME LADADITAALAACKAEGS--FKHGEFFTKIG------LKGKSAADIKKVF 47 sp|P02622|PRVB_GADCA LSNADIKAAEAACFKEGS--FDEDGFYAKVG------LDAFSADELKKLF 47 sp|P02619|PRVB_ESOLU -KDADVAAALAACSAADS--FKHKEFFAKVG------LASKSLDDVKKAF 46 sp|Q91482|PRVB1_SALSA CKEADIKTALEACKAADT--FSFKTFFHTIG------FASKSADDVKKAF 48 sp|P43305|PRVU_CHICK LSPSDIAAALRDCQAPDS--FSPKKFFQISG------MSKKSSSQLKEIF 48 sp|P20472|PRVA_HUMAN LNAEDIKKAVGAFSATDS--FDHKKFFQMVG------LKKKSADDVKKVF 48 sp|P80079|PRVA_FELCA LGAEDIKKAVEAFTAVDS--FDYKKFFQMVG------LKKKSPDDIKKVF 48 sp|P02627|PRVA_RANES LAAGDISKAVSAFAAPES--FNHKKFFELCG------LKSKSKEIMQKVF 47 sp|P02626|PRVA_AMPME IPEADINKAIHAFKAGEA--FDFKKFVHLLG------LNKRSPADVTKAF 47 sp|P02586|TNNC2_RABIT PTKEELDAIIEEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECF 100 :: : :. * * : : * sp|P02620|PRVB_MERME GIIDQDKSDFVEEDELKLFLQNFSAGARALTDAETATFLKAGDSDGDGKI 97 sp|P02622|PRVB_GADCA KIADEDKEGFIEEDELKLFLIAFAADLRALTDAETKAFLKAGDSDGDGKI 97 sp|P02619|PRVB_ESOLU YVIDQDKSGFIEEDELKLFLQNFSPSARALTDAETKAFLADGDKDGDGMI 96 sp|Q91482|PRVB1_SALSA KVIDQDASGFIEVEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMI 98 sp|P43305|PRVU_CHICK RILDNDQSGFIEEDELKYFLQRFECGARVLTASETKTFLAAADHDGDGKI 98 sp|P20472|PRVA_HUMAN HMLDKDKSGFIEEDELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKI 98 sp|P80079|PRVA_FELCA HILDKDKSGFIEEDELGFILKGFYPDARDLSVKETKMLMAAGDKDGDGKI 98 sp|P02627|PRVA_RANES HVLDQDQSGFIEKEELCLILKGFTPEGRSLSDKETTALLAAGDKDGDGKI 97 sp|P02626|PRVA_AMPME HILDKDRSGYIEEEELQLILKGFSKEGRELTDKETKDLLIKGDKDGDGKI 97 sp|P02586|TNNC2_RABIT RIFDRNADGYIDAEELAEIFR---ASGEHVTDEEIESLMKDGDKNNDGRI 147 : *.:..::: :** ::. :: * ::.* :.** * sp|P02620|PRVB_MERME GVEEFAAMV-----KG 108 sp|P02622|PRVB_GADCA GVDEFGALVDKWGAKG 113 sp|P02619|PRVB_ESOLU GVDEFAAMI-----KA 107 sp|Q91482|PRVB1_SALSA GIDEFAVLV-----KQ 109 sp|P43305|PRVU_CHICK GAEEFQEMV-----QS 109 sp|P20472|PRVA_HUMAN GVDEFSTLVA----ES 110 sp|P80079|PRVA_FELCA DVDEFFSLVA----KS 110 sp|P02627|PRVA_RANES GVDEFVTLVS----ES 109 sp|P02626|PRVA_AMPME GVDEFTSLVA----ES 109 sp|P02586|TNNC2_RABIT DFDEFLKMMEG---VQ 160. :** ::

29. READSEQ http://www.ebi.ac.uk/cgi-bin/readseq.cgi

31. Different Formats (PHYLIP)

32. PHYLIP (no gap)

33. Different Formats (MSF)

34. Converting Formats Can Be Dangerous Format conversion can result in data loss After converting your file, you must make sure your data is still intact The following slide shows the most common losses that occur during conversion

35. Potential Information Loss When Converting MSAs

36. Editing your MSA If your MSA looks bad... Don’t torture the online server Edit the MSA yourself locally Never, ever, ever (ever) use a standard word processor Always use a dedicated MSA editor The most popular online tool is Jalview You can get it at www.jalview.org

37. With Jalview You Can... Modify your MSA Remove some of the redundant sequences Insert/remove gaps Shift portions of the MSA Modify the alignment of a sub-group of sequences Recompute some portions of your alignment

41. Click a sequence to select

42. Drag to select columns

43. Some Special Features of Jalview Computation of a consensus sequence Computation of a phylogenetic tree Removal of the redundancy Applying any color scheme to your MSA

44. Preparing Your MSA for Publication MSAs in publications usually come with shaded colors You can improve your MSAs using online tools like Boxshade Boxshade will shade your MSA according to its degree of conservation

45. MSA => LOGO Graph A LOGO graph summarizes an MSA Tall letters indicate highly conserved positions Short letters indicate poorly conserved positions LOGO graphs are ideal for identifying conserved patterns weblogo.berkeley.edu/

46. Going Farther Your imagination is the limit when it comes to making MSAs nice- looking and informative Four very popular and easy-to-install MSA editors: CINEMA Seaview Belvu Kalignview Boxshade is the simplest shading tool If you need heavier capabilities, try Espript Available at espript.ibpc.fr

47. Molecular Evolution and Phylogenetic Reconstruction

48. Early Evolutionary Studies Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s The evolutionary relationships derived from these relatively subjective observations were often inconclusive. Some of them were later proved incorrect

49. Evolution and DNA Analysis: the Giant Panda Riddle For roughly 100 years scientists were unable to figure out which family the giant panda belongs to Giant pandas look like bears but have features that are unusual for bears and typical for raccoons, e.g., they do not hibernate In 1985, Steven O’Brien and colleagues solved the giant panda classification problem using DNA sequences and algorithms

50. Evolutionary Tree of Bears and Raccoons

51. Evolutionary Trees: DNA-based Approach 50 years ago: Emile Zuckerkandl and Linus Pauling brought reconstructing evolutionary relationships with DNA into the spotlight In the first few years after Zuckerkandl and Pauling proposed using DNA for evolutionary studies, the possibility of reconstructing evolutionary trees by DNA analysis was hotly debated Now it is a dominant approach to study evolution.

52. Who are closer?

53. Human-Chimpanzee Split?

54. Chimpanzee-Gorilla Split?

55. Three-way Split?

56. Out of Africa Hypothesis Around the time the giant panda riddle was solved, a DNA-based reconstruction of the human evolutionary tree led to the Out of Africa Hypothesis that c laims our most ancient ancestor lived in Africa roughly 200,000 years ago

57. Human Evolutionary Tree (cont’d) http://www.mun.ca/biology/scarr/Out_of_Africa2.htm

58. The Origin of Humans: ”Out of Africa” vs Multiregional Hypothesis Out of Africa: Humans evolved in Africa ~150,000 years ago Humans migrated out of Africa, replacing other shumanoids around the globe There is no direct descendence from Neanderthals Multiregional: Humans evolved in the last two million years as a single species. Independent appearance of modern traits in different areas Humans migrated out of Africa mixing with other humanoids on the way There is a genetic continuity from Neanderthals to humans

59. mtDNA analysis supports “Out of Africa” Hypothesis African origin of humans inferred from: African population was the most diverse (sub-populations had more time to diverge) The evolutionary tree separated one group of Africans from a group containing all five populations. Tree was rooted on branch between groups of greatest difference.

60. Evolutionary Tree of Humans: (microsatellites) Neighbor joining tree for 14 human populations genotyped with 30 microsatellite loci.

61. Human Migration Out of Africa http://www.becominghuman.org 1. Yorubans 2. Western Pygmies 3. Eastern Pygmies 4. Hadza 5. !Kung 1 2 3 4 5

62. Two Neanderthal Discoveries Feldhofer, Germany Mezmaiskaya, Caucasus Distance: 2500km

63. Two Neanderthal Discoveries Is there a connection between Neanderthals and today’s Europeans? If humans did not evolve from Neanderthals, whom did we evolve from?

64. Multiregional Hypothesis? May predict some genetic continuity from the Neanderthals through to the Cro- Magnons up to today’s Europeans Can explain the occurrence of varying regional characteristics

65. Sequencing Neanderthal’s mtDNA mtDNA from the bone of Neanderthal is used because it is up to 1,000x more abundant than nuclear DNA DNA decay overtime and only a small amount of ancient DNA can be recovered (upper limit: 100,000 years) PCR of mtDNA (fragments are too short, human DNA may mixed in)

66. Neanderthals vs Humans: surprisingly large divergence AMH vs Neanderthal: 22 substitutions and 6 indels in 357 bp region AMH vs AMH only 8 substitutions

67. Evolutionary Trees How are these trees built from DNA sequences? leaves represent existing species internal vertices represent ancestors root represents the oldest evolutionary ancestor

68. Reading Your Tree There’s a lot of vocabulary in a tree Nodes correspond to common ancestors The root is the oldest ancestor Often artificial Only meaningful with a good outgroup Trees can be un-rooted Branch lengths are only meaningful when the tree is scaled Cladograms are often scaled Phenograms are usualy unscaled

69. Rooted and Unrooted Trees In the unrooted tree the position of the root (“oldest ancestor”) is unknown. Otherwise, they are like rooted trees

70. Type of Trees (Cladogram)

71. Type of Trees (Phylogram)

72. 3 Ways to Use Your Tree Finding the closest relative of your organism Usually done with a tree based on the ribosomal RNA Discovering the function of a gene Finding the orthologues of your gene Finding the origin of your gene Finding whether your gene comes from another species

73. Evolutionary Rate Normal mutation rate is 1 in 10-8 nucleotides Normal Polymorphic VarianceApproximately 1 in every 1000 nucleotides This is the background on which evolutionary changes are analyzed.

74. Orthology and Paralogy Orthologous genes Separated by speciation Often have the same function Paralogous genes Separated by duplications Can have different functions In the graph: A is paralogous with B A1 is orthologous with A2 直系（垂直）同源和旁系（平行） 同源

75. Working on the Right Data Garbage in  garbage out The quality of your tree depends on the quality of the data Your first task is to assemble a very accurate MSA

76. DNA or Proteins Most phylogenetic methods work on Proteins and DNA sequences If possible, always compute a multiple-sequence alignment on the protein sequences Translate the sequences if the DNA is coding Align the sequences Thread the DNA sequences back onto the protein MSA with coot.embl.de/pal2nal If your DNA sequences are coding and have more than 70% identity... Compute the tree on the DNA multiple-sequence alignment If your DNA sequences are coding and have less than 70% identity... Compute the tree on the protein multiple-sequence alignment

77. Which Sequences ? Orthologous sequences Produce a species tree Show how the considered species have diverged Paralogous sequences Produce a gene tree Show the evolution of a protein family

78. Establishing Orthology Establishing orthology is very complicated It is common practice to establish orthology using the best reciprocal BLAST A is a gene of Genome X B is a gene of Genome Y BLAST (Gene A against Genome X) = B BLAST (Gene B against Genome Y) = A A is B’s best friend and B is A’s best friend… Phylogeny purists dislike this method

79. Creating the Perfect Dataset

80. Building the Right MSA Your MSA should have as few gaps as possible. Most time should remove columns with gaps. Some variability but not too much! Some conservation but not too much!

81. Building the Right Tree There are three types of tree-reconstruction methods Distance-based methods Statistical methods Parsimony methods Statistical methods are the most accurate Maximum likelihood of success Bayesian methods Statistical methods take more time Limited to small datasets

82. Distance-based method Compute a distance matrix Try to fit the matrix to a tree Fast but may not be very accurate

83. Distances in Trees Edges may have weights reflecting: Number of mutations on evolutionary path from one species to another Time estimate for evolution of one species into another In a tree T, we often compute d ij (T) - the length of a path between leaves i and j d ij (T) – tree distance between i and j

84. Distance in Trees: an Exampe d 1,4 = 12 + 13 + 14 + 17 + 12 = 68 i j

85. Distance Matrix Given n species, we can compute the n x n distance matrix D ij D ij may be defined as the edit distance between a gene in species i and species j, where the gene of interest is sequenced for all n species. D ij – edit distance between i and j

86. Edit Distance vs. Tree Distance Given n species, we can compute the n x n distance matrix D ij D ij may be defined as the edit distance between a gene in species i and species j, where the gene of interest is sequenced for all n species. D ij – edit distance between i and j Note the difference with d ij (T) – tree distance between i and j

87. Compute a Distance Matrix Evolutionary Distance - number of substitutions per 100 amino acids (for proteins) or nucleotides (for DNA) A C T G T A G G A A T C G C A A T G A A A G A A T C G C A C T G T A G G A A T C G C A C T G C A G G A A T A G C A A T G A A A G A A T C G C 3 observed changes 6 actual changes

88. Edit Distance vs Tree Distance d 1,4 = 12 + 13 + 14 + 17 + 12 = 68 D 1,4 may be smaller than 68, as some changes may not be observed i j

89. Fitting Distance Matrix Given n species, we can compute the n x n distance matrix D ij Evolution of these genes is described by a tree that we don’t know. We need an algorithm to construct a tree that best fits the distance matrix D ij

90. Fitting Distance Matrix Fitting means D ij = d ij (T) Lengths of path in an (unknown) tree T Edit distance between species (known)

91. Reconstructing a 3 Leaved Tree Tree reconstruction for any 3x3 matrix is straightforward We have 3 leaves i, j, k and a center vertex c Observe: d ic + d jc = D ij d ic + d kc = D ik d jc + d kc = D jk

92. Reconstructing a 3 Leaved Tree (cont’d) d ic + d jc = D ij + d ic + d kc = D ik 2d ic + d jc + d kc = D ij + D ik 2d ic + D jk = D ij + D ik d ic = (D ij + D ik – D jk )/2 Similarly, d jc = (D ij + D jk – D ik )/2 d kc = (D ki + D kj – D ij )/2

93. Trees with > 3 Leaves An tree with n leaves has 2n-3 edges This means fitting a given tree to a distance matrix D requires solving a system of “n choose 2” equations with 2n-3 variables This is not always possible to solve for n > 3

94. Additive Distance Matrices Matrix D is ADDITIVE if there exists a tree T with d ij (T) = D ij NON-ADDITIVE otherwise

95. Distance Based Phylogeny Problem Goal: Reconstruct an evolutionary tree from a distance matrix Input: n x n distance matrix D ij Output: weighted tree T with n leaves fitting D If D is additive, this problem has a solution and there is a simple algorithm to solve it

96. Using Neighboring Leaves to Construct the Tree Find neighboring leaves i and j with parent k Remove the rows and columns of i and j Add a new row and column corresponding to k, where the distance from k to any other leaf m can be computed as: D km = (D im + D jm – D ij )/2 Compress i and j into k, iterate algorithm for rest of tree

97. Finding Neighboring Leaves To find neighboring leaves we simply select a pair of closest leaves.

98. Finding Neighboring Leaves To find neighboring leaves we simply select a pair of closest leaves. WRONG

99. Finding Neighboring Leaves Closest leaves aren’t necessarily neighbors i and j are neighbors, but (d ij = 13) > (d jk = 12) Finding a pair of neighboring leaves is a nontrivial problem!

100. Neighbor Joining Algorithm In 1987 Naruya Saitou and Masatoshi Nei developed a neighbor joining algorithm for phylogenetic tree reconstruction Finds a pair of leaves that are close to each other but far from other leaves: implicitly finds a pair of neighboring leaves Advantages: works well for additive and other non- additive matrices, it does not have the flawed molecular clock assumption