1. Phylogenetics

2. Branch length may or may not reflect distance or time

3. A B C A B C A B C Three possible rooted trees with three taxa
(unrooted tree has no meaning with 3 taxa)

4. A B A C A D A B C D C D B D C B
Four possible unrooted trees with four taxa

5. Characters and character states
Important terms
Characters and character states
Ancestral versus derived
(not primitive versus advanced)

6. Invertebrate Humans Dogs Birds Fish animals
Some examples of ancestral and derived characters
Invertebrate
Humans
Dogs
Birds
Fish
animals
Upright
posture
loss of
body hair
feathers and
feathered wings
bony limbs
bony skeleton
nervous system
Note:
Ancestral and derived are relative terms. In this tree, a character
state is derived with respect to states at nodes lower in the tree.

7. The problem of taxonomy not reflecting phylogeny

8. polyphyletic groups contain taxa that are not derived from a single common ancestor
Old groups “algae” and “fungi” were polyphyletic
Brown
Algae
Oomycete
Fungi
Green
Plants
Green
Algae
True
Fungi
animals

9. paraphyletic (subset of polyphyletic) groups have a taxonomic group contained within another group of equal status

10. Old “Reptilian” class is paraphyletic
Crocodiles
Birds
Lizards

11. Crotalus (snake)
Lineage: Tetrapoda; Amniota; Sauropsida; Sauria; Lepidosauria; 
Squamata; Bifurcata; Unidentata; Episquamata; Toxicofera; Serpentes; Colubroidea; Viperidae; Crotalinae
Lepidosauria = superorder
Corvus (bird)
Lineage: Tetrapoda; Amniota; Sauropsida; Sauria; Archelosauria; 
Archosauria; Dinosauria; Saurischia; Theropoda; Coelurosauria; Aves; 
Neognathae; Passeriformes; Corvoidea; Corvidae
Crocodylus (crocodile)
Archosauria; Crocodylia; Longirostres; Crocodylidae

12. Estimating phylogeny (phylogenetics)
Distance methods (not “phylogenetic”)
- Examples: UPGMA, Fitch-Margolish, Neighbor Joining
- Begin with a single measure of similarity or distance for every pair of taxa
“Phylogenetic” methods (presume some model for evolution)
- Examples: parsimony, maximum likelihood, Bayesian (Mr.Bayes)
- Look at multiple discrete characters and use differences among character states to infer phylogeny

13. Molecular approaches to phylogenetics begin with multiple sequence alignments  

14. Multiple sequence alignment and tree formats

15. FASTA format
>C8_9737 AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTACTACCTGAATC TGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGA GG AACACCTCAGTAT GCGATTTAC------GAATTG AATATCCAGGATTCGAGTTCTGCTCCCACGGTGTATAGCGGTCCGACTCCGAGTGGGAAC TCAAACCTTGCGGCAGTGTAC---TTTTCCCCGAACAAAGACAGATTCATCATATTCTCG AACACTGACACA----CGTCATTACCTATACTGGGTTAATTCCACCCTTCAGAGTGGTGA GTAATCCTACGCTATCGTTATACTTTATATAATTGGTA---TAAGCTGATAGTACCCCAC AGCAAACCGAATTTCGGGCACTGGTAGCGTTATGAGTGCCAGCCCACTGGCCGCGACTAC AATAACGAACGTGCA-GACGAGGTCTATGACTATCTTTTTGTACTACATGGACGTCAACA CCCTCCTTAACCGAATTGTCGGAAAGGTCACAGACAATGAAATTCATTGGTATGCAAACC AGGTCGTTGAAGGCGCTCCCCCGATGAAGGTGGACACGCTATTGACGGGCGTGG--TTGT TGAGGGAAAATGGAACTGCTTGTATTACATCCCAGATGGAGACACGGAGTTCAGGGCGTT TA >C1_3006 AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTACTACCTGAATC TGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGA GG AACACCTCAGTAT GCGATTTAC------GAATTG AATATCCAGGATTCGAGTTCTGCTCCCACGGTGTATAGCGGTCCGACTCCGAGTGGGAAC TCAAACCTTGCGGCAGTGTAC---TTTTCCCCGAACAAAGACAGATTCATCATATTCTCG AACACTGACACA----CGTCATTACCTATACTGGGTTAATTCCACCCTTCAGAGTGGTGA GTAATCCTACGCTATCGTTATACTTTATATAATTGGTA---TAAGCTGATAGTACCCCAC AGCAAACCGAATTTCGGGCACTGGTAGCGTTATGAGTGCCAGCCCACTGGCCGCGACTAC AATAACGAACGTGCA-GACGAGGTCTATGACTATCTTTTTGTACTACATGGACGTCAACA CCCTCCTTAACCGAATTGTCGGAAAGGTCACAGACAATGAAATTCATTGGTATGCAAACC AGGTCGTTGAAGGCGCTCCCCCGATGAAGGTGGACACGCTATTGACGGGCGTGG--TTGT TGAGGGAAAATGGAACTGCTTGTATTACATCCCAGATGGAGACACGGAGTTCAGGGCGTT TA

16. CLUSTAL 2.1 multiple sequence alignment C2_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C4_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C6_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C8_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C9_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C13_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C5_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C11_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C18_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C3_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C17_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C14_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C15_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C16_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C10_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C12_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C1_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC C7_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAATTGGGTCTACTACCTGAATCTGC ************************************************************ C2_ CACAGTATGCCGCCCGTCTTTTGGGCTAACG-CCTTTAAGCTAACCAAAGCTACAGAGGA C4_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C6_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C8_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C9_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C13_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C5_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C11_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C18_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C3_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAACCTAACCAAAGCTACAGAGGA C17_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAACCTAACCAAAGCTACAGAGGA C14_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C15_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C16_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C10_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C12_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C1_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA C7_ CACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTTAAGCTAACCAAAGCTACAGAGGA ******************************* ******* ********************
Clustal format

17. (“interleaved in this case”)
C8_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C1_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C6_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C16_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C15_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C4_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C18_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C17_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C9_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C11_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C3_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C12_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C14_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C2_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C13_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C5_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C7_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC ChglobMRA ATCAACCCGG CCCAGGTTGC AACCACCAAC TTGACGGGTG TTGAGCTCGT C10_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACG-CCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT CGTCGGCTTC ACCAACAAGC TGCCGGCCGC CGCCGCCAAC GACTGCAAAT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT
Phylip format
(“interleaved in this case”)

18. #NEXUS BEGIN DATA; dimensions ntax=19 nchar=722; format missing=
#NEXUS BEGIN DATA; dimensions ntax=19 nchar=722; format missing=? interleave datatype=DNA gap= -; matrix C6_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C3_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C8_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C1_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C7_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C9_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C5_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C10_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C13_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C12_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C11_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C18_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C4_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C16_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C15_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C17_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C2_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC C14_ AGGCACTGGCCGCGTGTTCTGACAATGACCGTAACAA---TTGGGTCTAC ChglobMRA ATCAACCCGGCCCAGGTTGCAACCACCAACTTGACGGGTGTTGAGCTCGT C6_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C3_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C8_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C1_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C7_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C9_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C5_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C10_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C13_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C12_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C11_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C18_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C4_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C16_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C15_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C17_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT C2_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACG-CCTTT C14_ TACCTGAATCTGCCACAGTATGCCGCCCGTCTTTTGGGCTAACGGCCTTT ChglobMRA CGTCGGCTTCACCAACAAGCTGCCGGCCGCCGCCGCCAACGACTGCAAAT
Nexus format

19. Newick tree file format
(((C9_0369: ,(((C17_8945: ,C2_4297: ): ,C14_7898: ): ,(((ChglobMRA: ,(C16_0071: ,C15_0317: ): ): ,(C6_3957: ,C3_9861: ): ): ,(C12_9837: ,((((C4_3894: ,C18_5945: ): ,(C11_4708: ,C13_0459: ): ): ,C10_4233: ): ,C5_8725: ): ): ): ): ): ,C7_9443: ): ,C1_3006: ,C8_9737: ):0.0;

20. Newick format dictates this tree

21. Newick tree format ( Physella_anatina/gb|AY651175.:0.00993,
Physa_heterostropha/gb|AY6511: ,
Physa_acuta/gb|AY |: )
: )
: ,
Physella_virgata/gb|AY : ,
Lymnaea_stagnalis/gb|EF : ,
Lymnaea_neotropica/emb|AM49400: )
: ,
Biomphalaria_glabrata/gi|34538: )
: );

22. Newick Nexus tree format

23. Estimating phylogeny (phylogenetics)
Distance methods (not “phylogenetic”)
- Examples: UPGMA, Fitch-Margolish, Neighbor Joining
- Begin with a single measure of similarity or distance for every pair of taxa
Phylogenetic methods (“phylogenetic”)
- Examples: parsimony, maximum likelihood, Bayesian (Mr.Bayes)
- Look at multiple discrete characters and use differences among character states to infer phylogeny

24. Distance (also sometimes called numerical) methods rely on a single numerical value that expresses the difference or similarity for any given pairwise comparison. With DNA data the similarity (or identity) value is usually obtained by dividing the number of matching nucleotide positions by the length of the two sequences being compared. (Distance can be calculated from similarity, for example by subtracting % similarity from 100, and vice versa)
Species 1:   CTGATCCGAGGTCAACCTTGGGTT-GTGAAGGTCGTTTTACGGCTGGAAC                 |||||||||||||||||||||| | | ||||||||||||||||||||||| species 2:      562 CTGATCCGAGGTCAACCTTGGGGTCGCGAAGGTCGTTTTACGGCTGGAAC 513

28. #
# Percent Identity Matrix - created by Clustal
1: C1_
2: C8_
3: C2_
4: C4_

29. # Percent Identity Matrix - created by Clustal

30. # Previous matrix values converted to distances (100 – x)

31. # Previous matrix values converted to distances (100 – x)
C. immitis
C. posadasii
C1_3006
C8_9737
C2_4297
C4_3894
Note that within species distances are smaller that between-species distances

32. Maximum likelihood tree

33. Estimating phylogeny (phylogenetics)
Distance methods (not “phylogenetic”)
- Examples: UPGMA, Fitch-Margolish, Neighbor Joining
- Begin with a single measure of similarity or distance for every pair of taxa
Phylogenetic methods (“phylogenetic”)
- Examples: parsimony, maximum likelihood, Bayesian (Mr.Bayes)
- Look at multiple discrete characters and use differences among character states to infer phylogeny

35. Character #2 - + - + - - + + - + + -
A B C D A C B D A D B C
Tree # Tree # Tree #3

36. From J. Felsenstein 1981 (J. Mol. Ecol. 17:368-376)
Maximum likelihood calculation

37. Maximum likelihood methods
RaxML, Phylip, others
Bayesian approaches
MrBayes, others
See posted presentations by Paul Lewis

38. Some potential pitfalls with molecular data

39. Paralogs versus Orthologs
Orthologs - homologous genes that reflect speciation
Paralogs - homologous genes that reflect gene duplication = members of a gene family in a single organism (examples: alpha versus beta hemoglobin; red versus green visual pigment proteins
Important to distinguish between these when doing comparative analyses (It’s sometimes hard to tell)

40. The Problem of Multiple Hits

41. Actual
Divergence
Measured
Time

43. Among other problems, this causes “long-branch attraction”
(For protein coding genes it is often better to use amino-acid sequences when substantial genetic distances are involved)

44. Scoring in phylogenetic methods is model dependent

45. Bootstrap Analysis

46. (“interleaved in this case”)
C8_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C1_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C6_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C16_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C15_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C4_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C18_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C17_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C9_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C11_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C3_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C12_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C14_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C2_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C13_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C5_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C7_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC ChglobMRA ATCAACCCGG CCCAGGTTGC AACCACCAAC TTGACGGGTG TTGAGCTCGT C10_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACG-CCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT CGTCGGCTTC ACCAACAAGC TGCCGGCCGC CGCCGCCAAC GACTGCAAAT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT
Phylip format
(“interleaved in this case”)

47. (“interleaved in this case”)
For example, the column in red could replace the first column
C8_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C1_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C6_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C16_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C15_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C4_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C18_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C17_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C9_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C11_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C3_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C12_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C14_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C2_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C13_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C5_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC C7_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC ChglobMRA ATCAACCCGG CCCAGGTTGC AACCACCAAC TTGACGGGTG TTGAGCTCGT C10_ AGGCACTGGC CGCGTGTTCT GACAATGACC GTAACAA--- TTGGGTCTAC TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACG-CCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT CGTCGGCTTC ACCAACAAGC TGCCGGCCGC CGCCGCCAAC GACTGCAAAT TACCTGAATC TGCCACAGTA TGCCGCCCGT CTTTTGGGCT AACGGCCTTT
Phylip format
(“interleaved in this case”)

48. Maximum likelihood tree
Numbers at nodes are bootstrap values
(% of bootstrap trees supporting the groupin above the node

49. Tree Rooting - midpoint (more or less assumes “molecular clock”) – usually doesn’t work all that well - Bring some outside information to bear on the question - better