Stuart M' Brown

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Molecular PhylogeneticsComputing Evolution
presented by

• Stuart M. Brown
• New York University School of Medicine

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Topics

• A. Molecular Evolution
• B. Calculating Distances
• C. Clustering Algorithms
• D. Cladistic Methods
• E. Computer Software

Portions of this lecture have been inspired by
web pages created by Dr. Brian Golding,
Department of Biology, McMaster University,
Hamilton, Ontario, Canada, L8S 4K1
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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

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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?

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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).

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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.

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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.

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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.

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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)

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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 ..t t..
Each nucleotide difference is a character
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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.

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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.
• 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)

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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.

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Biodiversity and Conservation

• Phylogenetics also overlaps significantly with
  other branches of evolutionary biology.
• Check out the Tree of Life project for an
  introduction to phylogenetics and its
  relationship to biodiversity.
• http//phylogeny.arizona.edu/tree/phylogeny.html
• Measurements of DNA sequence differences are now
  being used to implement plans for the
  conservation of genetic resources.

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Disclaimers

• Before describing any theoretical or practical
  aspects of phylogenetics, it is necessary to give
  some disclaimers. This area of computational
  biology is an intellectual minefield!
• Neither the theory nor the practical applications
  of any algorithms are universally accepted
  throughout the scientific community.
• The application of different software packages to
  a data set is very likely to give different
  answers minor changes to a data set are also
  likely to profoundly change the result.

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Are there Correct trees??

• Despite all of these caveats, it is actually
  quite simple to use computer programs calculate
  phylogenetic trees for data sets.
• Provided the data are clean, outgroups are
  correctly specified, appropriate algorithms are
  chosen, no assumptions are violated, etc., can
  the true, correct tree be found and proven to be
  scientifically valid?
• Unfortunately, it is impossible to ever
  conclusively state what is the "true" tree for a
  group of sequences (or a group of organisms)
  taxonomy is constantly under revision as new data
  is gathered.

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A modern revision of the seals and sea lions
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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).

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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.

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Darwin was a Cladist

• The natural system based on descent with
  modification the characters that naturalists
  consider as showing true affinity are those which
  have been inherited from a common parent, and in
  so far as all true classification is
  genealogical that community of descent is the
  common bond that naturalists have been seeking.
• - Charles Darwin, Origin of Species, 1859

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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.

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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.

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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.

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Computing a Distance Matrix
Reading sequences... gtr1_human 548
total, 548 read gtr2_human 548 total,
548 read gtr3_human 548 total, 548
read gtr4_human 548 total, 548 read
gtr5_human 548 total, 548
read Computing distances using Kimura method...
1 x 2 48.61 1 x 3 45.50 1
x 4 65.74 1 x 5 107.70 2 x 3
61.53 2 x 4 74.57 2 x 5 113.82
3 x 4 68.93 3 x 5 104.43 4 x 5
110.86
Matrix 1 1 2
3 4 5 ________________________
____________________________________ ..
1 0.00 48.61 45.50 65.74
107.70 2 0.00
61.53 74.57 113.82 3
0.00 68.93 104.43
4 0.00
110.86 5
0.00
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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.

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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

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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.
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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.

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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.

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Neighbor Joining

• The Neighbor Joining method is the most popular
  way to build trees from distance measurements
• (Saitou and Nei 1987, Mol. Biol. Evol. 4406)
• 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. 6514 (1989)

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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.

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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

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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.

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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)

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• 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 2
Tree 1
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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.

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Assumptions for Maximum Likelihood

• The frequencies of DNA transitions (Clt-gtT,Alt-gtG)
  and transversions (C or Tlt-gtA 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.

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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 tress based on
  Neighbor Joining.
• MacClade is a well designed cladistics program
  that allows the user to explore possible trees
  for a data set.

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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/phyli
  p-uk.html
• Louxin Zhang at the Natl. University of
  Singapore has a WebPhylip server
• http//sdmc.krdl.org.sg8080/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//iu
  bio.bio.indiana.edu/treeapp/treeprint-form.html

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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.htm
  l
• University of California, Berkeley Museum of
  Paleontology (UCMP)
• http//www.ucmp.berkeley.edu/clad/clad4.html

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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.
  ten 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.

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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.