1. Available at http://planet.uwc.ac.za/nisl DNA variation in Ecology and Evolution DNA variation in Ecology and Evolution IV- Clustering methods and Phylogenetic reconstruction Maria Eugenia D’Amato BCB 705:Biodiversity

2. Organization of the presentation Phylogenetic reconstruction Networks Multivariate analysis Distance ML MP

3. Characters: Characters: independent homologous Continuous Discrete Binary Multistate

4. DNA sequence characters Alignment = hypothesizing of a homology relationship for each site Sequence comparison BLAST search - GenBank Coding sequenceblastn blastx Non-coding DNA blastn

5. Blast search results Score E Score E Sequences producing significant alignments: (Bits) Value gi|87299397|dbj|AB239568.1| Mantella baroni mitochondrial ND5... 101 3e-18 gi|343991|dbj|D10368.1|FRGMTURF2 Rana catesbeiana mitochondri... 97.6 5e-17 gi|14209845|gb|AF314017.1|AF314017 Rana sylvatica NADH dehydr... 93.7 8e-16 The lower the E-value, the better the alignment GeneBank Accession numbers for the sequence Species that match the query

6. Blast search results >gi|87299397|dbj|AB239568.1| Mantella baroni mitochondrial ND5, ND1, ND2 genes for NADH dehydrogenasegi|87299397|dbj|AB239568.1| subunit 5, NADH dehydrogenase subunit 1, NADH dehydrogenase subunit 2, complete cds Length=10814 Identities = 99/115 (86%), Gaps = 0/115 (0%) Strand=Plus/Minus 5’end Score = 101 bits (51), Expect = 3e-18 Query 451 TTAGTTGAGGATTAAATTTTAGGATAATAACTATTCAGCCGAGGTGGCTGATGGAAGAAA 510 ||||||||||||||||||||| ||||||| ||||||||| ||||| | |||||||| | Sbjct 10203 TTAGTTGAGGATTAAATTTTAAAATAATAAGTATTCAGCCCAGGTGACCAATGGAAGAGA 10144 Query 511 AAGCTAAAATTTTACGTAGTTGTGTTTGGCTAATGCCGCCTCATCCGCCTACAAG 565 | |||| ||||||||||||||| |||||| |||| || ||||| || |||||||| Sbjct 10143 AGGCTATAATTTTACGTAGTTGAGTTTGGTTAATACCCCCTCAACCTCCTACAAG 10089 Description of the genes contained in the sequence with this Accession number Strands aligned alignment

7. Phylogenetic reconstruction Phylogenetic reconstruction Distance methods C1 C2 C3 C4 C5 C6 C7 1234512345 Distance criterion Similarity / dissimilarity criterion dendrogram 5 x 5 5 X 7

8. Distances criterion for binary data a a + b + c a = bands common to a and b b = bands exclusive to a c = bands exclusive to b J = (x1, y1) (x2, y2) Jaccard’s distance Manhattan distance M = P1 P2  (x1-x2) 2 + (x2-y2) 2 Euclidean distance

9. Distance criterion for DNA data- Distance criterion for DNA data- Models of DNA susbstitution p = n of different nucleotides/ total n nucleotides f AA f AC f AG f AT f CA f CC f CG f CT f GA f GC f GG f GT f TA f TC f TG f TT Fxy = a b c d e f g h i j k l m n o p Fxy =

10. Models of DNA susbstitution Jukes and Cantor D = 1 – ( a + f + k + p) dxy = - ¾ ln (1- 4/3 D) F81 B = 1 – (  2 A +  2 C +  2 G +  2 T ) dxy = - B ln (1- D/B) Equal rate Unequal base freqs K2P P = c + h + i + nTransitions Q = b + d + e + g + j + l + m + oTransversions 1 1-2P-Q dxy = 1 ln 2 1 ln 1 4 1-2Q +

11. Distances criterion for diploid data Dn -ln Jx i y i  Jx i Jy i Nei 1972 = I Jx = xi 2 Jx = yi 2 Jxy = xiyi Cavalli Sforza 1967 Darc =  (1/L)  (2  /  ) 2  = cos -1   xiyi

12. Phylogenetic reconstruction criterion for distance data V1 V2 V3 V4 V5 A B C D Additive tree (NJ) Ultrametric tree (UPGMA) A B C V1 V2 V3 V4 Properties dAB = v1 + v2 dAC = v1 + v3 + v4 dAD = v1 + v3 + v5 dBC = v2 + v3 + v5 dCD = v4 + v5 dAB = v1 + v2 + v3 dAC = v1 + v2+ v4 dBC = v3 + v4 v3 = v4 v1 = v2 + v3 = v2 = v4

13. Maximum Likelihood (1) (1)C….GGACACGTTTA….C (2) (2)C….AGACACCTCTA….C (3) (3)C….GGATAAGTTAA….C (4) (4)C….GGATAGCCTAG….C 1 J n 4 3 2 1 C ACG C ACG 6 5 4321 Unrooted tree Tree after rooting at an internal node Lj = Prob A A C ACG + Prob A C + Prob……. L = L 1 x L 2 x L 3 …x L N. =  Lj LnL = ln L 1 + ln L 2 + …. L N =  ln Lj L D = Pr (D H)

14. Hypothesis testing Hypothesis testing Likelihood ratio test  = log L 1 – log L 0 Rate variation Appropriate substitution Model 2   2 distribution d.f. = N sequences in the tree –2; or d.f = difference number of parameters H1 and H0

15. Bootstrapping Bootstrapping H ow well supported are the groups? Trumpet fish

16. Maximum Parsimony Minimize tree length To obtain rooted trees (and character polarity) use an outgroup. The ingroup is monophyletic. 1 1ATATT 2 2ATCGT 3 3GCAGT 4 4GCCGT Tree (first site) 1 2 3 4 1 change 5 changes G G AG A A G G GA A A

17. C Maximum Parsimony- Maximum Parsimony- example C T C T T Site 2Site 3 AAC A A C C C C C AA Site 4 T G G G GG Site 5 No changes TT T T T T Tree length L =  k i=1 li

18. Maximum parsimony: Maximum parsimony: example Sites 1 2 3 4 5 Total ((1,2),(3,4)) 1 1 2 1 0 5 ((1,3),(2,4)) 2 2 1 1 0 6 ((1,4),(2,3)) 2 2 2 1 0 7 Tree Phylogenetically informative sites

19. Networks Phylogenetic representation allowing reticulation More appropriate for intraespecific data Ancestor is alive hybridization, recombination, horizontal transfer, polyploidization agct acat acct acatagctacct 1 2 3 4 5 6 7

20. Multivariate clustering C1 C2 C3 C4 C5 C6 C7 1234512345 5 X 7 similarity criterion correlations 7 x 7 Calculate eigenvectors with highest eigenvalues Project data onto new axes (eigenvectors) X 1 st axis Y 2 nd axis Z 3 rd axis