MOLECULAR EVOLUTION MB437 and ADVANCES IN MOLECULAR EVOLUTION MB537

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MOLECULAR EVOLUTION MB437 and ADVANCES IN
MOLECULAR EVOLUTION MB537 Marcie McClure, Ph.D.
,mars_at_parvati.msu.montana.edu, 994-7370
Fall, 2006, Tu/Th 1100-1215 Cooley-B2 Lectur
e 1 8/29/06 Organization Introduction
What is molecular evolution? Lecture 2 8/31/0
6 The BIG BANG and formation of the
elements necessary for life. Lecture 3 9/5/06
Biogenesis I The primitive earth and the
prebiotic soup. Lecture 4 9/7/06
Biogenesis II Self-assembly, Energetics
and Bioinformational Molecules.
Lecture 5 9/12/06
Biogenesis III Protein or Nucleic Acids first?
RNA or DNA? Lecture 6 9/14/06
The RNA world the three Domains of life and
LUCA. Lecture 7 9/19/06 Origin of the Ge
netic Code and more on LUCA Lecture 8 9/21/06
Genomes Content and
Architecture. Lecture 9 9/26/06
Mutation nucleotide substitutions and
amino acid replacements. Lecture 10 9/28/06
class canceled Lecture 11 10/3/06 Method
s Analyzing sequences rates/patterns.
Lecture 12 10/5/06 open discussion
Lecture 13 10/10/06. Molecular Phylogeny I
History, terms, definitions, and limits.
Lecture 14 10/12/06 Molecular Phylogeny II
How to determine a phylogenetic tree.
Lecture 15 10/17/06 Molecular Phylogeny III
Improvements and Extensions to Genome Trees.
Lecture 16 10/19/06 Molecular Phylogeny IV
Bayesian Approaches to plylogenetic
reconstruction Lecture 17 10/24/06 open di
scussion Lecture 18 10/26/06 Deviation from
Tree-like behavior horizontal transmission of
information Lecture 19 10/31/06 Convergent
Evolution the antifreeze story.
Lecture 20 11/2/06 Evolution of Viruses
Lecture 21 11/7/06 UNIVERSITY HOLIDAY
Lecture 22 11/9/06 Retroid Agents
eukaryotic hosts and disease states.
Lecture 23 11/14/06 Bioethics of the
Human Genome Project/ Introduction to
Bioinformatics. Lecture 24 11/16/06 Example
s of in silico research I the RNA polymerase
story. Lecture 25 11/21/06 11/22-24/06
THANKSGIVING HOLIDAY Lecture 26 11/28/06 Lec
ture 27 12/30/06 Lecture 28 12/5/06 Lect
ure 29 12/7/06
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Nomenclature of phylogenetic reconstruction

• Internal and external nodes
• Rooted and unrooted
• Scaled and unscaled
• Topology
• Branch lengths

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• Phylogenetic trees are
• Monophyletic all taxon originate from a
• common ancestor and said grouping includes
• the ancestor and all descendents
• 2) Paraphyletic all taxon originate from a
• common ancestor and said grouping includes
• the ancestor but not all descendents
• 3) Polyphyletic does not include the
• common ancestor in the group

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Searching tree spaceLooking for THE tree among
the forest of possibilitiesCan you actually
fine the TRUE ?
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• How are phylogenetic trees refined?
• subtree pruning and regrafting
• tree bisection and reconstruction
• sequential addition
• star composition

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Phylogenetic trees trace history from the
common day descent to the ancester.
Trees can reflect phylogenetic history at variou
s levels.
What levels can trees based on sequences be made?
Gene
Species
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Now that you HAVE a multiple alignment of a bios
equence. Count the differences between each pai
r, distance or similarity. Weight the differenc
es according to a specific model?
Compare the values. Now reconstruct the histo
ry of the biosequences WHAT ? HOW?
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There are two basic data types

• Character data
• B) Distance data

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Character Data Char data
provide information about a OTUs 1) can
assume one of two or more mutually exclusive
states example independent chars
length of nose 2) quantitative chars are
continuous measured on a continuous scale
3) qualitative chars are discrete and can be
assigned two or more values a) binaryonly one
of two char states are possible b) multistate
when three or more states are possibleexample
in sequences qualitative multistate chars are
the positions in the sequence while the actual
nucleotide(1/4) or AA(1/20) are the char states
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Explicit assumptions about char data
evolutiona) the number of discrete steps
required to change one char into anotherb) the
probability with which a change may occurc)
chars are unordered if changing from one char to
another takes only one stepexample nucleotides
takes only one step to change from any of the
fourd) chars are ordered if it takes
intermediate steps to change one into anothere)
chars are partially ordered when the number of
steps varies for different pairwise
combinations of chars states example amino
acids f) most discrete characters in molecular
evolution are reversibleg) plesiomorphy is a
primitive or ancestral char stateh) apomorphy is
a derived char state that is evolutionary novel
compared to the ancestral statei) homoplasya
char that has arisen independently in several
lineages
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Distance Data Distance data
provide quantitative information about the
similarity /dissimilarity 1) distance data
cannot be converted to char data but char data
can be converted into distance data
a sequence string, which is char
data is not of use BUT 2) by providing some
measure of difference/similarity between pairs
of sequences many methods exist to use
these values to infer phylogenetic relationships
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A Phylogenetic tree is a hypothesis of how
current day, extant OTUs whatever they maybe,
evolved from a common ancestor.

• A hypothesis must be tested.

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• Assumptions of the models
• Changes in different copies of genes are
  independent.
  
• Changes at each site are independent.
• All sites changes at the same rate.
• All bases are equally frequent.
• REALITY
• 1) The same gene in different organisms in the
  same environment
  
• may well change in a similar manner
  (parallelism)
  
• 2) Gene products are three dimensional objects
  with both short and long range
  
• interactions. Some sites certainly do not change
  independently o other.
  
• 3) Functionally important sites change are more
  highly constrained, therefore,
  
• not all sites change at the same rate,
• 4) Bases are not equally distributed in the
  genome.
  

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Basic Phylogenetic Inference Methods
1) In distance matrix methods evolutionary dista
nce is computed by counting all the
nucleic acid or protein substitutions for all
pairwise relationships of multiple alignment.
UPGMA, (unweighted/weighted/ pair group method w
ith arithmetic means, and various offshoots),
employs a sequential clustering algorithm. The
distance values are identified in the order of
similarity and a tree is built in a stepwise man
ner. The most closely related sequences are
joined by a node and then the next most closely
related sequences are added, etc. As the
connected set of sequences accumulate they are t
reated as a composite set. 2) Maximum parsimony
methods try to find the most efficient path
between two evolutionary states. This approach
is based on finding the minimum number of
mutations to explain the differences among the s
equences. An initial tree topology is specified
and each position in sequence examined in suppor
t of each tree. All reasonable topologies are
examined until a tree with minimal numbers of ch
anges is chosen and designated the best tree.
3) Maximum likelihood is similar to the maximum
parsimony approach in that it checks every
reasonable tree topology and examines the support
for each tree by every sequence. The best
tree is the one which maximizes the probability
of the sequences having been generated by
the pathway specified by th tree. This is
computationally intensive method that is
supported by the PHYLIP package of Felsenstein.
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Evolutionary Informative Sites
For example to infer an MP tree
1) identify all informative sites 2) sum the ch
anges over all inform sites for each trees
3) the tree with the fewest changes to account
for the observed data is the MP tree
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Rooting trees1) out-group
must not be too distant or distance estimates
will be unreliableexample if mammals then use
marsupials as an out-group birds can only be
used if gene is highly conserved2) out-group
must have an external measure that guarantees
that it is really an out-group3) multiple out
groups can increase reliability of distance
estimate if they are not too far
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Assessing tree reliabilityTrees are
statistically inferred, therefore, trees and
component parts should be assessed in a
statistical mannerThere are two basic
questions a) Which parts of this tree are
statistically robust ? b) Is this tree
statistically better than other trees?
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Problems in phylogenetic
reconstruction strengths and
weaknessesa) UPGMA type methods rate
consistency should holdb) additive methods
(neighbor-joining) are not good for multiple hits
or very distant relationshipsc)
MP are best because these methods only calculate
shortest path downside for distance sequences
parallelism will be under-
estimated if rates vary significantly
these methods are not robust due to the
long-branch attraction which is random
similarity due to long periods of divergence
among some members of a clade
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