BCB 444544 Introduction to Bioinformatics

1
BCB 444/544 - Introduction to Bioinformatics
Lab (Oct 5) Background Phylogenetic
Methods Oct 5
2
Background Phylogenetic Methods
Multiple Sequence Alignment
Building Phylogenetic Trees
3
Multiple Sequence Alignment (MSA) Motivation

• Correspondence. Find out which parts do the same
  thing
• Similar genes are conserved across widely
  divergent species, often performing similar
  functions
• Protein Structure prediction
• Use knowledge of structure of one or more members
  of a protein MSA to predict structure of other
  members
• Structure is more conserved than sequence
• Create profiles for protein families
• Allow us to search for other members of the
  family
• Genome Assembly Automated reconstruction of
  contig maps of genomic fragments such as ESTs
• MSA is the starting point for phylogenetic
  analysis

4
Multiple Sequence Alignment

• VTISCTGSSSNIGAG?NHVKWYQQLPG
• VTISCTGTSSNIGS??ITVNWYQQLPG
• LRLSCSSSGFIFSS??YAMYWVRQAPG
• LSLTCTVSGTSFDD??YYSTWVRQPPG
• PEVTCVVVDVSHEDPQVKFNWYVDG??
• ATLVCLISDFYPGA??VTVAWKADS??
• ATLVCLISDFYPGA??VTVAWKADS??
• AALGCLVKDYFPEP??VTVSWNSG-??
• VSLTCLVKGFYPSD??IAVEWESNG-?

• Goal Bring the greatest number of similar
  characters into the same column of the alignment
• Similar to alignment of two sequences.

5
Multiple Sequence Alignment Approaches

• Optimal Global Alignments -Dynamic programming
• Generalization of Needleman-Wunsch
• Find alignment that maximizes a score function
• Computationally expensive Time grows as product
  of sequence lengths
• Global Progressive Alignments - Match
  closely-related sequences first
• Global Iterative Alignments - Multiple
  re-building attempts to find best alignment

6
Dynamic Programming MSA General Case

• For k sequences of length n, dynamic programming
  algorithm does (2k-1) nk operations
• Example 6 sequences of length 100 require
  6.4X1013 calculations
• Space for table is nk
• Implementations (e.g., WashU MSA 2.1) use tricks
  and only search subset of dynamic programming
  table
• Even this is expensive. E.g., Baylor CM Search
  launcher limits MSA to 8 sequences of 800
  characters and 10 minutes processing time

7
What is a phylogeny?
www.rci.rutgers.edu/dvhowe/ invertzoo/lecture1_20
06slides.pdf
8
Phylogenetic (evolutionary) trees
Describe evolutionary relationships between
species
or
Cannot be known with certainty!
Nevertheless, phylogenies can be useful
9
Applications of Phylogenetic Analysis

• Inferring function
• Closely related sequences occupy neighboring
  branches of tree
• Tracking changes in rapidly evolving populations
  (e.g., viruses)
• Which genes are under selection?

10
Methods

• Distance-based
• Parsimony
• Maximum likelihood

11
Distance Matrices
a
b
c
d
12
Least Squares
13
Methods

• Distance-based
• Parsimony
• Maximum likelihood

14
Parsimony
Goal Find the tree with least number of
evolutionary changes
a, b
f
c
d
e
d
15
Methods

• Distance-based
• Parsimony
• Maximum likelihood

16
Markov models on trees

• Observed The species labeling the leaves
• Hidden The ancestral states
• Transition probabilities The mutation
  probabilities
• Assumptions
• Only mutations are allowed
• Sites are independent
• Evolution at each site occurs according to a
  Markov process

17
Models of evolution at a site

• Transition probability matrix M mij, i, j
  ?A, C, T, G where mij Prob(i ? j mutation
  in 1 time unit)
• Different branches of tree may have different
  lengths

18
The probability of an assignment
T
G
T
A
G
C
T
Probability mTG mGA mGG mTT mTC mTT
19
Ancestral reconstruction most likely assignment
X
Y
Z
A
G
C
T
L maxX,Y,Z mXY mYA mYG mXZ mZC mZT