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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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.
• By looking at gene sequences, one can see
  evolution in action.

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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 of organisms based on
  their evolutionary history.

• Cladistics was developed by Willi Hennig, a
  German entomologist, in 1950.

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DNA is a good tool for taxonomy

• DNA sequences have many advantages over
  morphological taxonomic characters
• Character states can be scored unambiguously
• Large numbers of characters can be scored for
  each individual

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• New alleles are created by mutations in the DNA
  sequence

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Definition

• Homology related by descent
• Homologous sequence positions

? ATTGCGC
ATTGCGC
ATTGCGC
?
ATTGCGC
? ATCCGC
AT-CCGC
C
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Dynamic Programming

• Dynamic Programming is a very general programming
  technique.
• It is applicable when a large search space can be
  structured into a succession of stages, such
  that
• the initial stage contains trivial solutions to
  sub-problems
• each partial solution in a later stage can be
  calculated by recurring a fixed number of partial
  solutions in an earlier stage
• the final stage contains the overall solution

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Global vs. Local Alignments

• Global alignment algorithms start at the
  beginning of two sequences and add gaps to each
  until the end of one is reached.
• Local alignment algorithms finds the region (or
  regions) of highest similarity between two
  sequences and build the alignment outward from
  there.

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Phylogenetics starts with Multiple Alignment

• Can only align homologous sequences
• Pairwise alignment can be exact
• exhaustive computation dynamic programming
• Multiple alignment is always approximate
  (heuristic) - the problem increases
  exponentially with the number of sequences to be
  aligned
• Progressive pairwise method
• Software of choice is CLUSTAL (emma in EMBOSS)

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Clustal Format

• CLUSTAL X (1.81) multiple sequence alignment
• CAS1_BOVIN MKLLILTCLVAVALARPKHPIKHQGLPQ------
  --EVLNEN-
• CAS1_SHEEP MKLLILTCLVAVALARPKHPIKHQGLSP------
  --EVLNEN-
• CAS1_PIG MKLLIFICLAAVALARPKPPLRHQEHLQNEPDSR
  E--------
• CAS1_HUMAN MRLLILTCLVAVALARPKLPLRYPERLQNPSESS
  E--------
• CAS1_RABBIT MKLLILTCLVATALARHKFHLGHLKLTQEQPESS
  EQEILKERK
• CAS1_MOUSE MKLLILTCLVAAAFAMPRLHSRNAVSSQTQ----
  --QQHSSSE
• CAS1_RAT MKLLILTCLVAAALALPRAHRRNAVSSQTQ----
  ---------
• .. .

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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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Gap Penalties

• Linear gap penalties Affine gap penalties
• p (o l.e)
• Gap opening /Gap extension
• Penalized multiple nearby gaps
• Protein specific penalties (on by default)
• Increase the probability of gaps associated with
  certain residues
• Increase the chances of gaps in loop regions (gt
  5 hydrophilic residues)

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Multiple Alignment Tips

• Align pairs of sequences using an optimal method
• Progressive alignment programs such as ClustalX
  for multiple alignment
• Choose representative sequences to align
  carefully
• Choose sequences of comparable lengths
• Progressive alignment programs may be combined
• Review alignment by eye and edit
• If you have a choice align amino acid sequences
  rather than nucleotides

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Genome Data

• Recent genome sequencing projects have
  contributed a huge store of data to publicly
  accessible databases
• These data make it quite easy to conduct
  phylogentic experiments from any internet
  connected computer.

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Caculating Trees from Sequences

• First, collect a set of homologous sequences.
• Make a multiple alignment (line them up)
• Calculate differences (distance matrix)
• Join pairs with the fewest differences
• Then join groups
• Repeat until all sequences are included in the
  tree
• Draw a pretty looking tree.

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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 - 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)

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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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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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Ancestral gene
A
(globin)
Duplication
A
B
(myoglobin)
(hemoglobin)
Speciation
A2
B2
A1
B1
(mouse)
(human)
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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 CLUSTAL
  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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Other Methods

• Distance methods do not make use of all
  information in an alignment
• differences between each pair of sequences are
  summarized as a single number
• each character has information
• Parsimony allows the use of all known
  evolutionary information in building a tree
• Maximum Likelyhood is even more computationally
  intense (creates a tree for each character)
• These methods are beyond the scope of this course

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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.
• 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.
• EMBOSS emma multiple alignment, many dozens of
  phylogeny programs (includes entire PHYLIP
  package fprotdis, fdrawtree, etc)

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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.
  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.

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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.
• Using several alternate methods can give an
  indication of the robustness of a given
  conclusion.
• Bootstrapping methods can show if your results
  depend on just a small part of the data, or are
  generally supported
• Analyze several different 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.