1. BLS 303: Principles of Computational Biology
Lecture 1: Molecular Phylogenetics

2. Topics
• i. Molecular Evolution
• ii. Calculating Distances
• iii. Clustering Algorithms
• iv. Cladistic Methods
• v. Computer Software

3. Evolution
• The theory of evolution is the
foundation upon which all of
modern biology is built.
• From anatomy to behavior to genomics, the
scientific method requires an appreciation of
changes in organisms over time.
• It is impossible to evaluate relationships among
gene sequences without taking into consideration
the way these sequences have been modified over
time

4. Relationships
Similarity searches and multiple alignments of
sequences naturally lead to the question:
“How are these sequences related?”
and more generally:
“How are the organisms from which
these sequences come related?”

5. Taxonomy
• The study of the relationships between groups of
organisms is called taxonomy, an ancient and
venerable branch of classical biology.
• Taxonomy is the art of classifying things into
groups — a quintessential human behavior —
established as a mainstream scientific field by
Carolus Linnaeus (1707-1778).

7. Phylogenetics
• Evolutionary theory states that groups of similar
organisms are descended from a common ancestor.
• Phylogenetic systematics (cladistics) is a method
of taxonomic classification based on their
evolutionary history.
• It was developed by Willi Hennig,
a German entomologist, in 1950.

8. Cladistic Methods
• Evolutionary relationships are documented by
creating a branching structure, termed a phylogeny
or tree, that illustrates the relationships between the
sequences.
• Cladistic methods construct a tree (cladogram) by
considering the various possible pathways of
evolution and choose from among these the best
possible tree.
• A phylogram is a tree with branches that are
proportional to evolutionary distances.

10. Molecular Evolution
• Phylogenetics often makes use of numerical data,
(numerical taxonomy) which can be scores for
various “character states” such as the size of a
visible structure or it can be DNA sequences.
• Similarities and differences between organisms can
be coded as a set of characters, each with two or
more alternative character states.
• In an alignment of DNA sequences, each position
is a separate character, with four possible character
states, the four nucleotides.

11. DNA is a good tool for taxonomy
DNA sequences have many advantages
over classical types of taxonomic
characters:
– Character states can be scored unambiguously
– Large numbers of characters can be scored for
each individual
– Information on both the extent and the nature of
divergence between sequences is available
(nucleotide substitutions, insertion/deletions, or
genome rearrangements)

12. A aat tcg ctt cta gga atc tgc cta
atc ctg
B ... ..a ..g ..a .t. ... ... t..
... ..a
C ... ..a ..c ..c ... ..t ... ...
... t.a
D ... ..a ..a ..g ..g ..t ... t.t
Each nucleotide difference is a character
..t t..

13. Sequences Reflect Relationships
• After working with sequences for a while, one develops an
intuitive understanding that “for a given gene, closely related
organisms have similar sequences and more distantly related
organisms have more dissimilar sequences. These
differences can be quantified”.
• Given a set of gene sequences, it should be possible to
reconstruct the evolutionary relationships among genes
and among organisms.

15. What Sequences to Study?
• Different sequences accumulate changes at
different rates - chose level of variation that is
appropriate to the group of organisms being
studied.
– Proteins (or protein coding DNAs) are constrained by
natural selection - better for very distant relationships
– Some sequences are highly variable (rRNA spacer
regions, immunoglobulin genes), while others are
highly conserved (actin, rRNA coding regions)
– Different regions within a single gene can evolve at
different rates (conserved vs. variable domains)

16. (globin) Ancestral gene
A
Duplication
(hemoglobin) A B (myoglobin)
Speciation
A1 B1 A2 B2
(mouse) (human)

17. Orthologs vs. Paralogs
• When comparing gene sequences, it is important
to distinguish between identical vs. merely similar
genes in different organisms.
• Orthologs are homologous genes in different
species with analogous functions.
• Paralogs are similar genes that are the result of a
gene duplication.
– A phylogeny that includes both orthologs and paralogs
is likely to be incorrect.
– Sometimes phylogenetic analysis is the best way to
determine if a new gene is an ortholog or paralog to
other known genes.

18. Terminologies of phylogeny
• Phylogenetic (binary) tree: A tree is a graph composed of
nodes and branches, in which any two nodes are connected
by a unique path.
• Nodes: Nodes in phylogenetic trees are called taxonomic
units (TUs) Usually, taxonomic units are represented by
sequences (DNA or RNA nucleotides or amino acids).
• Branches: Branches in phylogenetic trees indicate
descent/ancestry relationships among the TUs.
• Terminal (external) nodes: The terminal nodes are also
called the external nodes, leaves, or tips of the tree and are
also called extant taxonomic units or operational taxonomic
units (OTUs)

19. Terminologies of phylogeny
• Internal nodes: The internal nodes are nodes, which are
not terminal. They are also called ancestral TUs.
• Root: The root is a node from which a unique path leads to
any other node, in the direction of evolutionary time. The
root is the common ancestor of all TU’s under study.
• Topology: The topology is the branching pattern of a tree.
• Branch length: The lengths of the branches determine the
metrics of a tree. In phylogenetic trees, lengths of branches
are measured in units of evolutionary time.

20. Example of phylogenetic tree: VP1 gene for FMDV

21. Genes vs. Species
• Relationships calculated from sequence data represent
the relationships between genes, this is not necessarily
the same as relationships between species.
• Your sequence data may not have the same
phylogenetic history as the species from which they
were isolated.
• Different genes evolve at different speeds, and there is
always the possibility of horizontal gene transfer
(hybridization, vector mediated DNA movement, or
direct uptake of DNA).

22. Cladistic vs. Phenetic
Within the field of taxonomy there are two
different methods and philosophies of building
phylogenetic trees: cladistic and phenetic
– Phenetic methods construct trees (phenograms) by
considering the current states of characters without
regard to the evolutionary history that brought the
species to their current phenotypes.
– Cladistic methods rely on assumptions about
ancestral relationships as well as on current data.

23. Phenetic Methods
• Computer algorithms based on the phenetic model rely on
Distance Methods to build of trees from sequence data.
• Phenetic methods count each base of sequence difference
equally, so a single event that creates a large change in
sequence (insertion/deletion or recombination) will move two
sequences far apart on the final tree.
• Phenetic approaches generally lead to faster algorithms and
they often have nicer statistical properties for molecular data.
• The phenetic approach is popular with molecular
evolutionists because it relies heavily on objective character
data (such as sequences) and it requires relatively few
assumptions.

24. Cladistic Methods
• For character data about the physical traits of
organisms (such as morphology of organs etc.)
and for deeper levels of taxonomy, the cladistic
approach is almost certainly superior.
• Cladistic methods are often difficult to
implement with molecular data because all of
the assumptions are generally not satisfied.

25. Distances Measurements
• It is often useful to measure the genetic distance between
two species, between two populations, or even between
two individuals.
• The entire concept of numerical taxonomy is based on
computing phylogenies from a table of distances.
• In the case of sequence data, pairwise distances must be
calculated between all sequences that will be used to build
the tree - thus creating a distance matrix.
• Distance methods give a single measurement of the
amount of evolutionary change between two sequences
since divergence from a common ancestor.

26. DNA Distances
• Distances between pairs of DNA sequences are relatively
simple to compute as the sum of all base pair differences
between the two sequences.
– this type of algorithm can only work for pairs of sequences that are
similar enough to be aligned
• Generally all base changes are considered equal
• Insertion/deletions are generally given a larger weight than
replacements (gap penalties).
• It is also possible to correct for multiple substitutions at a
single site, which is common in distant relationships and
for rapidly evolving sites.

28. Amino Acid Distances
• Distances between amino acid sequences are a bit more
complicated to calculate.
• Some amino acids can replace one another with relatively little
effect on the structure and function of the final protein while
other replacements can be functionally devastating.
• From the standpoint of the genetic code, some amino acid
changes can be made by a single DNA mutation while others
require two or even three changes in the DNA sequence.
• In practice, what has been done is to calculate tables of
frequencies of all amino acid replacements within families of
related protein sequences in the databanks: i.e. PAM and
BLOSSUM

29. The PAM 250 scoring matrix
A R N D C Q E G H I L K M F P S T W Y V
A 2
R -2 6
N 0 0 2
D 0 -1 2 4
C -2 -4 4 -5 4
Q 0 1 1 2 -5 4
E 0 -1 1 3 -5 2 4
G 1 -3 0 1 -3 -1 0 5
H -1 2 2 1 -3 3 1 -2 6
I -1 -2 -2 -2 -2 -2 -2 -3 -2 5
L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6
K -1 3 1 0 -5 1 0 -2 0 -2 -3 5
M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6
F -4 -4 -4 -6 -4 -5 -5 -5 -2 1 2 -5 0 9
P 1 0 -1 -1 -3 0 -1 -1 0 -2 -3 -1 -2 -5 6
S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 3
T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -2 0 1 3
W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17
Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10
V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4
Dayhoff, M, Schwartz, RM, Orcutt, BC (1978) A model of evolutionary change in proteins. in Atlas of Protein
Sequence and Structure, vol 5, sup. 3, pp 345-352. M. Dayhoff ed., National Biomedical Research Foundation,
Silver Spring, MD.

30. Clustering Algorithms
Clustering algorithms use distances to calculate
phylogenetic trees. These trees are based solely on
the relative numbers of similarities and differences
between a set of sequences.
– Start with a matrix of pairwise distances
– Cluster methods construct a tree by linking the least
distant pairs of taxa, followed by successively more
distant taxa.

31. UPGMA
• The simplest of the distance methods is the UPGMA
(Unweighted Pair Group Method using Arithmetic averages)
• The PHYLIP programs DNADIST and PROTDIST
calculate absolute pairwise distances between a group of
sequences. Then the GCG program GROWTREE uses
UPGMA to build a tree.
• Many multiple alignment programs such as PILEUP use a
variant of UPGMA to create a dendrogram of DNA
sequences which is then used to guide the multiple alignment
algorithm.

32. Neighbor Joining
• The Neighbor Joining method is the most popular
way to build trees from distance measurements
(Saitou and Nei 1987, Mol. Biol. Evol. 4:406)
– Neighbor Joining corrects the UPGMA method for its (frequently
invalid) assumption that the same rate of evolution applies to each
branch of a tree.
– The distance matrix is adjusted for differences in the rate of
evolution of each taxon (branch).
– Neighbor Joining has given the best results in simulation studies
and it is the most computationally efficient of the distance
algorithms (N. Saitou and T. Imanishi, Mol. Biol. Evol. 6:514 (1989)

33. Cladistic methods
• Cladistic methods are based on the assumption that a
set of sequences evolved from a common ancestor by
a process of mutation and selection without mixing
(hybridization or other horizontal gene transfers).
• These methods work best if a specific tree, or at least
an ancestral sequence, is already known so that
comparisons can be made between a finite number of
alternate trees rather than calculating all possible trees
for a given set of sequences.

34. Parsimony
• Parsimony is the most popular method for
reconstructing ancestral relationships.
• Parsimony allows the use of all known evolutionary
information in building a tree
– In contrast, distance methods compress all of the
differences between pairs of sequences into a single
number

35. Building Trees with Parsimony
• Parsimony involves evaluating all possible trees
and giving each a score based on the number of
evolutionary changes that are needed to explain
the observed data.
• The best tree is the one that requires the fewest
base changes for all sequences to derive from a
common ancestor.

36. Parsimony Example
• Consider four sequences: ATCG, TTCG,
ATCC, and TCCG
• Imagine a tree that branches at the first
position, grouping ATCG and ATCC on
one branch, TTCG and TCCG on the other
branch.
• Then each branch splits, for a total of 3
nodes on the tree (Tree #1)

37. Compare Tree #1 with one that first divides ATCC on its own
branch, then splits off ATCG, and finally divides TTCG from
TCCG (Tree #2).
Trees #1 and #2 both have three nodes, but when all of the
distances back to the root (# of nodes crossed) are summed,
the total is equal to 8 for Tree #1 and 9 for Tree #2.
Tree Tree
#1 #2

38. Maximum Likelihood
• The method of Maximum Likelihood attempts to
reconstruct a phylogeny using an explicit model of
evolution.
• This method works best when it is used to test (or
improve) an existing tree.
• Even with simple models of evolutionary change,
the computational task is enormous, making this
the slowest of all phylogenetic methods.

39. Assumptions for Maximum Likelihood
• The frequencies of DNA transitions (C<->T,A<->G) and
transversions (C or T<->A or G).
• The assumptions for protein sequence changes are taken
from the PAM matrix - and are quite likely to be violated in
“real” data.
• Since each nucleotide site evolves independently, the tree is
calculated separately for each site. The product of the
likelihood's for each site provides the overall likelihood of
the observed data.

40. Computer Software for Phylogenetics
Due to the lack of consensus among evolutionary biologists
about basic principles for phylogenetic analysis, it is not
surprising that there is a wide array of computer software
available for this purpose.
– PHYLIP is a free package that includes 30 programs that compute
various phylogenetic algorithms on different kinds of data.
– The GCG package (available at most research institutions) contains
a full set of programs for phylogenetic analysis including simple
distance-based clustering and the complex cladistic analysis
program PAUP (Phylogenetic Analysis Using Parsimony)
– CLUSTALX is a multiple alignment program that includes the
ability to create trees based on Neighbor Joining.
– DNAStar
– MacClade is a well designed cladistics program that allows the user
to explore possible trees for a data set.

41. Phylogenetics on the Web
• There are several phylogenetics servers available
on the Web
– some of these will change or disappear in the near future
– these programs can be very slow so keep your sample sets small
• The Institut Pasteur, Paris has a PHYLIP server at:
http://bioweb.pasteur.fr/seqanal/phylogeny/phylip-uk.html
• Louxin Zhang at the Natl. University of Singapore has a WebPhylip server:
http://sdmc.krdl.org.sg:8080/~lxzhang/phylip/
• The Belozersky Institute at Moscow State University has their own
"GeneBee" phylogenetics server:
http://www.genebee.msu.su/services/phtree_reduced.html
• The Phylodendron website is a tree drawing program with a nice user
interface and a lot of options, however, the output is limited to gifs at
72 dpi - not publication quality.
http://iubio.bio.indiana.edu/treeapp/treeprint-form.html

42. Other Web Resources
• Joseph Felsenstein (author of PHYLIP) maintains a
comprehensive list of Phylogeny programs at:
http://evolution.genetics.washington.edu/phylip
/software.html
• Introduction to Phylogenetic Systematics,
Peter H. Weston & Michael D. Crisp, Society of Australian Systematic
Biologists
http://www.science.uts.edu.au/sasb/WestonCrisp.html
• University of California, Berkeley Museum of
Paleontology (UCMP)
http://www.ucmp.berkeley.edu/clad/clad4.html

43. Software Hazards
• There are a variety of programs for Macs and PCs,
but you can easily tie up your machine for many
hours with even moderately sized data sets (i.e.
fifty 300 bp sequences)
• Moving sequences into different programs can be
a major hassle due to incompatible file formats.
• Just because a program can perform a given
computation on a set of data does not mean that
that is the appropriate algorithm for that type of
data.

44. Conclusions
Given the huge variety of methods for computing
phylogenies, how can the biologist determine what
is the best method for analyzing a given data set?
– Published papers that address phylogenetic issues generally
make use of several different algorithms and data sets in order
to support their conclusions.
– In some cases different methods of analysis can work
synergistically
• Neighbor Joining methods generally produce just one tree, which can
help to validate a tree built with the parsimony or maximum likelihood
method
– Using several alternate methods can give an indication of the
robustness of a given conclusion.