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. 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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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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Protein Evolution

• Protein sequences can be used to study more
  distant evolutionary relationships
• Related proteins have "conserved" substitutions
  (amino acids with similar biochemical properties).

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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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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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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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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 list of related genes
• Make a multiple alignment
• Compute a matrix of pairwise distances
• Clustering methods construct a tree by linking
  the least distant pairs, followed by successively
  more distant pairs.

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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 distance 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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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.

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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.