1. Phylogenetic trees School B&I TCD Bioinformatics May 2010

2. Why do trees?

3. Phylogeny 101 OTUsoperational taxonomic units: species, populations, individuals Nodes internal (often ancestors) Nodes external (terminal, often living species, individuals) Branches length scaled (length propn evo dist) Branches length unscaled, nominal, arbitrary Outgroupan OTU that is most distantly related to all the other OTUs in the study. Choose outgroup carefully

4. Phylogeny 102 Trees rooted N=(2n-3)! / 2 n-2 (n-2)! Trees unrooted N=(2n-5)! / 2 n-3 (n-3)! OTUs #rooted trees #unrooted trees 211 331 4153 510515 6954105 710395954 813513510395 92027025135135 10343494252027025 2034*10 6 8*10 21

5. Four key aspects of tree A DC B A B C D Topology Branch lengths Root Confidence A B C D Basic tree D C B A D C B A 100 78

6. Distances from sequence Use Phylip Protdist or DNAdist D= non-ident residues/total sequence length Correction for multiple hits necessary because Jukes-Cantor assumes all subs equally likely Kimura: transition rate NE transversion rate Ts usually > Tv G AA

7. Methods Distance matrix –UPGMA –Neighbour joining NJ Maximum parsimony MP –tree requiring fewest changes Maximum likelihood ML –Most likely tree Bayesian: sort of ML –Samples large number of “pretty good” trees

8. Trees NJ Distance matrix Neighbor joining is very fast Often a “good enough” tree Embedded in ClustalW

9. Trees MP Maximum parsimony Minimum # mutations to construct tree Better than NJ – information lost in distance matrix – but much slower Sensitive to long-branch attraction –Long branches clustered together No explicit evolutionary model Protpars refuses to estimate branch lengths Informative sites

10. Long-branch attraction True tree MusHBA MusHBB HumHBB HumHBA Rodents evolve faster than primates False “LBA” tree MusHBA MusHBB HumHBA HumHBB

11. Maximum parsimony Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * * It is a good alignment clearly aligning homologous sites without gaps. Here we have a representative alignment. Want to determine the phylogenetic relationships among the OTUs

12. There are 3 possible trees for 4 taxa (OTUs): 1 3 1 2 1 2 \_____/ \_____/ \_____/ / \ / \ / \ 2 4 3 4 4 3 Or (1,2)(3,4) (1,3)(2,4) and (1,4)(2,3) Aim to identify (phylogenetically) informative sites and use these to determine which tree is most parsimonious.

13. The identical sites 1, 6, 8 are useless for phylogenetic purposes. Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

14. Site 2 also useless: OTU1’s A could be grouped with any of the Gs. Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

15. Site 4 is uniformative as each site is different. UNLESS transitions weighted in which case (1,4)(2,3) Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

16. For site 3 each tree can be made with (minimum) 2 mutations: Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

17. (1,2)(3,4) G A G A G A \ / \ / \ / G---A C---A A---A / \ / \ / \ C A C A C A

18. (1,3)(2,4) G C can do worse:G C \ / \ / A---A G---A / \ / \ A A

19. (1,4)(2,3) G C \ / A---A / \ A So site 3 is (Counterintuitively) NOT informative

20. Site 5, however, is informative because one tree shortest. Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

21. (1,2)(3,4) (1,3)(2,4) (1,4)(2,3) G A G G G G \ / \ / \ / G---A A---A G---G / \ / \ / \ G A A A A A

22. Likewise sites 7 and 9. By majority rule most parsimonious tree is (1,2)(3,4) supported by 2/3 informative sites. Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * *

23. Protpars infile: 8 370 BRU MSQNSLRLVE DNSV-DKTKA LDAALSQIER RLR ---------- ---V-DKSKA LEAALSQIER NGR ---------- -MSD-DKSKA LAAALAQIEK ECO ---------- AIDE-NKQKA LAAALGQIEK YPR ---------M AIDE-NKQKA LAAALGQIEK PSE ---------- -MDD-NKKRA LAAALGQIER TTH ---------- -MEE-NKRKS LENALKTIEK ACD ---------- -MDEPGGKIE FSPAFMQIEG

24. Protpars treefile: (((((ACD,TTH),(PSE,(YPR,ECO)) ),NGR),RLR),BRU);

25. outfile: One most parsimonious tree found: +-ACD +-------7 ! +-TTH +-6 ! ! +----PSE ! +----5 +-3 ! +-YPR ! ! +-4 ! ! +-ECO +-2 ! ! ! +-------------NGR --1 ! ! +----------------RLR ! +-------------------BRU remember: this is an unrooted tree! requires a total of 853.000 steps

26. Clustalw ****** PHYLOGENETIC TREE MENU ****** 1. Input an alignment 2. Exclude positions with gaps? = ON 3. Correct for multiple substitutions? = ON 4. Draw tree now 5. Bootstrap tree 6. Output format options S. Execute a system command H. HELP or press [RETURN] to go back to main menu

27. Trees General guidelines – NOT rules More data is better Excellent alignment = few informative sites Exclude unreliable data – toss all gaps? Use seqs/sites evolving at appropriate rate – Phylip DISTANCE – 3 rd positions saturated – 2 nd positions invariant – Fast evolving seqs for closely related taxa – Eliminate transition - homoplasy

28. Trees Beware base composition bias in unrelated taxa Are sites (hairpins?) independent? Are substitution rates equal across dataset? Long branches prone to error – remove them? –Choose outgroup carefully

29. Bootstrapping

30. Random re-sampling of the data –with replacement The MSA stays the same Each column of aligned residues in the MSA is a “site”. The sites are what is re-sampled.

31. Bootstrap 2 Having resampled the data –to get a new dataset/alignment –based on the original –the same length Redraw the tree from that dataset For each node –ask is this node retained in the resampled data. Re-iterate 100, 1000 or 10,000 times

32. Boostrap dataset Site: 1 2 3 4 5 6 7 8 9 OTU1 A A G A G T G C A OTU2 A G C C G T G C G OTU3 A G A T A T C C A OTU4 A G A G A T C C G * * * 4 OTUs and 9 “sites”

33. What do the little numbers mean?

34. Why does it work? The tree based on the real data is the best tree – the best estimate of what happened in evolution. If a node is based on many bits of info then some of these will be resampled If the node is based on a single site then it is unlikely to be resampled so we are less confident in that node.