PHYLOGENETIC TREES

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PHYLOGENETIC TREES

• Introduction to Computational Biology CIS 786
  With Dr. Barry Cohen
• Tuesday, May 7, 2001
• Paul Wood
• Yanchun Song
• Chaowei Sun

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Introduction
Paul Wood
Chaowei Sun
Yanchun Song
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What is a Phylogenetic Tree?

• Phylogenetic trees are representations of the
  similarity or dissimilarityamong both existing
  extinct living individuals across a set of
  characteristics or features.
• Similarity of molecular and physical systems
  provide compelling evidence that all life on
  earth arose from a common ancestry.

Carl R. Woese, Interpreting the universal
phylogenetic tree, Proc. Natl. Acad. Sci. USA,
Vol. 97, Issue 15, 8392-8396, July 18,
2000 http//www.pnas.org/cgi/content/full/97/15/83
92
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Why do we study Phylogenetic Trees?
because humans need to.fill in blanks
and understand in our own language
COMPARE

• Shall I thee to a
  summers day?
• W. Shakespeare, Sonnet 18
• There is a between
  Homer and Hesiod, between Æschylus and Euripides
• P. Shelley, Prometheus Unbound
• Life all around meAll in the loom, and oh
• What ! Woodlands,
  meadows,
• E. L. Masters, Spoon River Anthology
• If the foolish call them flowers/Need the wiser
  tell?
• // If the savants
  them/It is just as well.
• E. Dickenson, Part 1 Life, XCIV

SIMILARITY
PATTERNS
CLASSIFY
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What are some applications of phylogenetic
trees?

• Computational Linguistics
• Manning, Christopher D. and Heinrich Schutze,
  Foundations of Statistical Natural Language
  Processing, MIT Press, Cambridge Massachusetts,
  1999. http//www.aclweb.org/archive/fsnlp-ch1.pdf
• Archaeological Statistics
• Archaeological Statistics Brief Bibliography
  http//ad.trafficmp.com/tmpad/banner/itrack.asp?rv
  3.0id16nojs1
• Broad Historical and Technical Overview
• Discriminant Analysis and Clustering, Panel on
  Discriminant Analysis, Classification, and
  Clustering, Committee on Applied and Theoretical
  Statistics Board on Mathematical Sciences,
  Commission on Physical Sciences, Mathematics, and
  Resources National Research Council, NATIONAL
  ACADEMY PRESS, Washington, D.C. 1988
  http//www.ulib.org/webRoot/Books/National_Academy
  _Press_Books/discrim_analysis/discr001.htm

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Phylogenetic trees are used to study locations,
migrations, lives, health cultures of
populations.
Xenia
Katrina
Helena
Tara
Ursula
Velda
Jasmine
http//www.oxfordancestors.com/daughters.html
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Phylogenetic trees are used to study physical
genetic variability, evolution of species.
http//www.oxfordancestors.com/daughters.html
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Which areas of the genome provide mutant data to
create phylogenetic trees?
Autosomes
Mitochondrial Control Region
Y-Chromosome
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How do we get data for computational biology?
TISSUE
STEP 1 Eukaryotic Biochemical Protocol iskind
of like washing greasy dishes!
Homogenize

Detergent (Sodium Dodecyl Sulphate SDS)
High Weight
DNA

Concentration gradient
Phenol
DNA
DNA
Medium Weight
Genetic Material
Remove Upper Phase
DNA
DNA
RNA
SPIN 40 hrs _at_ 40,000 RPM
RNA
RNA
Insoluble Protein
RNA

RNA
Low Weight
Cesium Chloride
Cs
Cs
Cs
Phenol
Cs
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How do we get sequence data?
STEP 2 Cut up DNA using one of two methods
STEP 3 Label fragments using one of two
methods
Gel Electro- phoresis
2 a Sanger (Dideoxy)
4 Reactions
Restriction Enzymes
Fluorescent Dye
Fluorescence Spectroscopy
DNA
3a

DNA
atcgagtcc
DNA

DNA
DNA
EtOH
RNA
32Phosphate
Auto Radiography
3b
RNA
RNA
RNA
2 b Maxam-Gilbert
RNA
Gel Electro- phoresis
4 Reactions
Cs
Cs
Cs
Cs
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What is the rate of evolutionary changeorhow
many mutants can we expect?

• Estimates vary depending upon assessment method
  and location within the genome
• 134 independent mtDNA lineages spanning 327
  generations found 2.5 mutations per site per
  1000 yrs.
• A high observed substitution rate in the human
  mitochondrial DNA control region. Parsons TJ,
  Muniec DS, Sullivan K, Woodyatt N,
  Alliston-Greiner R, Wilson MR, Berry DL, Holland
  KA, Weedn VW, Gill P, Holland MM. Nat Genet 1997
  Apr 15(4)363-8. Armed Forces DNA
  Identification Laboratory, Armed Forces Institute
  of Pathology, Rockville, Maryland 20850, USA.
  http//www.mhrc.net/mitochondria.htm
• M. O. Dayhoff, R. M. Schwartz, and B. C. Orcutt.
  (1978) A model of evolutionary change in
  proteins. In Atlas of Protein Sequence and
  Structure, M. O. Dayhoff, (Ed.). National
  Biomedical Research Foundation, Vol. 5, Suppl. 3,
  chapter 22, 345-352)

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What do sequence data and input files typically
look like?
PHYLIP INPUT FILE (SEQUENCE)

• 282
• 1 AY053096 cacgggagct variable region... 282
• 2 AY053097 cacgggagct variable region... 282
• 3 AY053098 cacgggagct variable region... 282
• .
• 263
• !DomainData propertyCoding CodonStart1
• W._Pygmy_(1)_African TTC TTT CAT GGG
• W._Pygmy_(6)_African ... ... ... ...
• Kung_(7)_African ... .C. ... ... .T.
• Kung_(9)_African ... ... ... ... ...
• Kung_(10)_African ... ... ... ... ...
• Kung_(13)_African ... ... .G. ... ...

DISTANCE MATRIX
MEGA INPUT FILE (SEQUENCE)
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What are some of the major classifications of
algorithms software applications?
PHYLIP, PAUP MEGA are represented across most
categories. PHYLIP is the most widely
distributed and used. PAUP is most frequently
cited in publications. MEGA has a nice GUI and
is user friendly. http//evolution.genetics.washi
ngton.edu/phylip/software.html
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Yanchun Song
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Two Types of Data

• Distance-based
• The input is a matrix of distances between the
  species (e.g., the alignment score between them
  or the fraction of residues they agree on).
• Character-based
• Examine each character (e.g., a base in a
  specific position in the DNA) separately

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Pairwise Distance

• Model of Jukes and Cantor
• Each base in the DNA sequence has an equal chance
  of mutating, and when it does, it is replaced by
  some other nucleotide uniformly.
• Distance dij
• The fraction f of sites u where residues xui and
  xuj differ (presupposing an alignment of the two
  sequences).

T. H. Jukes and C. Cantor, Mammalian Protein
Metabolism, Chapter Evolution of protein
molecules, pages 21-132, Academic Press, New
York, 1969
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How to Make a Tree?

• Clustering methods
• UPGMA
• Neighbor-joining
• Parsimony

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Clustering Method UPGMA

• UPGMA Unweighted Pair Group Method with
  Arithmetic Mean
• Di,j between two clusters of species Ci and Cj
• d(p, q) distance function between species,
• ni Ci and nj Cj.

http//www.math.tau.ac.il/rshamir/algmb/00/scribe
00/html/lec08/node21.html
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Algorithm

• Initialization
• Initialize n clusters with the given species, one
  species per cluster.
• Size of each cluster ni ? 1 assign a leaf for
  each species.
• Iteration
• Find minimal Dij,
• Create a new cluster (ij), which has n(ij) ni
  nj members.
• Connect i and j to the new node (ij), each given
  length Di,j /2.
• Compute the distance from (ij) to all other
  clusters as a weighted average of the distances
  from its components
• Replace the columns and rows of clusters i and in
  D with cluster (ij), with D(ij),k computed as
  above.
• Termination
• until there is only one cluster left.

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UPGMA Example
http//www.icp.ucl.ac.be/opperd/private/upgma.htm
l
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UPGMA Example (contd)
D(A,B),C (DAC DBC) / 2 4 D(A,B),D (DAD
DBD) / 2 6 D(A,B),E (DAE DBE) / 2 6
D(A,B),F (DAF DBF) / 2 8
http//www.icp.ucl.ac.be/opperd/private/upgma.htm
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UPGMA Example (contd)
http//www.icp.ucl.ac.be/opperd/private/upgma.htm
l
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Additivity

• Given a tree, its edge lengths are said to be
  additive if the distance between any pair of
  leaves is the sum of the lengths of the edges on
  the path connecting them.

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Additivity

• Dim Dik Dkm
• Djm Djk Dkm
• Dij Dik Djk

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The idea of Neighbor-joining

• Distance of i from the rest of the tree
• To find neighboring nodes i and j
• min(Di,j (ui uj) )

R. Durbin, et al, Additivity and
neighbour-joining, Biological Sequence Analysis,
p. 169-173, Cambridge Univ. Press, 1999.
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Algorithm Neighbor-Joining

• Initialization
• Define T to be the set of leaf nodes, one for
  each given sequence, and put n T.
• Iteration
• For each species, compute .
• Choose a pair i, j in T for which Di,j (ui
  uj) is minimal.
• Join i and j to a new cluster k(ij). Calculate
  the branch lengths from i and j to the new node k
  as
• Di,k1/2(Di,j ui uj), Dj,k1/2(Di,j uj
  ui)
• Compute the distances between k and each other
  cluster
• Dk,m1/2(Di,m Dj,m Di,j), m?T
• Remove i and j from T and add k.
• Termination
• When T consists of only two nodes i and j,
  connect the remaining nodes by a branch of length
  Dij.

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Chaowei Sun
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MEGA 2

• Molecular Evolutionary Genetics Analysis
• Provides tools for exploring and analyzing DNA
  and protein sequences from evolutionary
  perspectives

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History of MEGA

• MEGA 1
• DOS-Based
• MEGA 2
• User-friendly interface
• Windows
• Macintosh
• Sun Workstation
• Linux

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Input

• Character Sequence
• - DNA/RNA
• - Protein
• Distance Matrix
• Import data from other formats, PHYLIP, XML, etc.

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Character Sequence
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Distance Matrix
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Methods and Algorithms

• methods for constructing phylogenetic trees from
  molecular data.
• 1. UPGMA Method
• 2. Neighbor-Joining (NJ) Method
• 3. Minimum Evolution (ME) Method
• 4. Maximum Parsimony (MP) Method

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Unweighted Pair Group Method with Arithmetic Mean
- UPGMA

• Assumes a constant rate of evolution
• sequential clustering method
• Produces a rooted tree
• edge lengths - time measured by a molecular clock

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Neighbor-Joining - NJ

• No assumption
• finds neighbors sequentially that may minimize
  the total length of the tree
• produces an unrooted tree
• root - midpoint of the longest route connecting
  two taxa in the tree

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Minimum Evolution - ME

• Finds a topology with the smallest sum of branch
  lengths
• time-consuming sum of branches for all
  topologies have to be evaluated

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Maximum Parsimony - MP

• Finds a topology that requires the smallest
  number of changes (substitution)
• For each topology sums up total number of
  substitutions

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Output - UPGMA
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Unrooted Tree - NJ
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Output - NJ
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Output - ME
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Comparison
Computational Method
Optimality criterion
Clustering algorithm
Parsimony
Characters
Minimum Evolution
UPGMA Neighbor-Joining
Distance
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Comparison Contd

• UPGMA, Neighbor-Joining
• Minimum Evolution, Maximum Parsimony

• Fast O(n2), Large dataset
• depends upon the order in which we add
  sequences to the tree

• Time consuming, NP-Complete
• use an explicit function relating the trees to
  the data

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The End
Thank you and enjoy the finals