Sequence Alignment

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Sequence Alignment
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Outline

• Alignment of pairs of sequence
• Local and global alignments
• Methods of alignments
• Dot matrix analysis
• Dynamic programming approach
• Use of scoring matrices and gap penalties
• Scoring matrices -- PAM and BLOSUM

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What is sequence alignment?

• Sequence alignment is a way of arranging the
  sequences of DNA, RNA or protein to identify
  regions of similarity that may be a consequence
  of functional, structural or evolutionary
  relationships between the sequences.
• The procedure of comparing two (pair-wise
  alignment) or more multiple sequences is to
  search for a series of individual characters or
  patterns that are in the same order in the
  sequences.
• There are two types of alignment local and
  global.

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Global alignment vs Local alignment

• Global alignment is attempting to match as much
  of the sequence as possible.
• The tool for Global alignment is based on
  Needleman-Wunsch algorithm.
• Local alignment is to try to find the regions
  with highest density of matches. The tool for
  local alignment is based on Smith-Waterman.
• Both algorithms are derivates from the basic
  dynamic programming algorithm.

L G P S S K Q T G K G S - S R I W D N
Global alignment L N -
I T K S A G K G A I M R L G D A - - - - - - - T
G K G - - - - - - - -
Local alignment - - - - - - - A G K
G - - - - - - - -
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Why do sequence alignment?

• Sequence alignment is useful for discovering
  structural, functional and evolutionary
  information in biological sequences.
• Sequences that are very much alike may have
  similar secondary and 3D structure, similar
  function and likely a common ancestral sequence.
  It is extremely unlikely that such sequences
  obtained similarity by chance.
• -- For DNA molecules with n nucleotides such
  probability is very low P 4-n.
• -- For proteins with n nucleotides, the
  probability even much lower P 20 n.
• Sequence alignment makes the following tasks
  easy 1.annotation of new sequences 2. modelling
  of protein structures 3. design and analysis of
  gene expression experiments

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An example of aligning text strings

• Raw Data ??? T C A T G C A T T G
• 2 matches, 0 gaps
• T C A T G C A T T G
• 3 matches (2 end gaps)
• T C A T G . . C A T
  T G

• 4 matches, 1 insertion
• T C A - T G . C
  A T T G
• 4 matches, 1 insertion
• T C A T - G .
  C A T T G

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Terminologies of sequence comparison

• Sequence identity -- exactly the same Amino Acid
  or Nucleotide in the same position.
• Sequence similarity -- Substitutions with similar
  chemical properties.
• Sequence homology -- general term that indicates
  evolutionary relatedness among sequences we
  usually measure of percentage identity of
  sequence homology
• Pairwise alignment -- used to find the
  best-matching piecewise (local) or global
  alignments of two query sequences. Pairwise
  alignments can only be used between two sequences
  at a time.
• Multiple sequence alignment -- try to align all
  of the sequences in a given query set.

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Methods of pairwise alignment

• Dot matrix analysis
• The dynamic programming (DP) algorithm
• Word methods

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What is Dot matrix analysis

• A dot matrix analysis is a method for comparing
  two sequences to look for possible alignment
  (Gibbs and McIntyre 1970)
• The algorithm for a dot matrix
• 1. One sequence (A) is listed across the top of
  the matrix and the other (B) is listed down the
  left side
• 2. Starting from the first character in B, one
  moves across the page keeping in the first row
  and placing a dot in many column where the
  character in A is the same
• 3. The process is continued until all possible
  comparisons between A and B are made
• 4. Any region of similarity is revealed by a
  diagonal row of dots
• 5. Isolated dots not on diagonal represent
  random matches

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What can Dot matrix analysis do?

• It can detect of matching regions can be
  improved by filtering out random matches and this
  can be achieved by using a sliding window
• It can be used to assess repetitiveness in a
  single sequence, such as direct and inverted
  repeats within the sequences

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1st example of Dot matrix analysis two identical
sequences

• http//arbl.cvmbs.colostate.edu/molkit/dnadot/inde
  x.html

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2nd example of Dot matrix analysis two very
different sequences

• http//arbl.cvmbs.colostate.edu/molkit/dnadot/ind
  ex.html

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3rd example of Dot matrix analysis two similar
sequences sequences

• http//arbl.cvmbs.colostate.edu/molkit/dnadot/inde
  x.html

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Dynamic programming algorithm

• The approach compares every pair of characters
  in the two sequences and generates an alignment,
  which is the best or optimal.
• The method can be useful in aligning nucleotide
  to protein sequences.
• The method requires large amounts of computing
  power and is a highly computationally demanding
  because the nature of dynamic programming
  technique is recursion.
• New algorithmic improvements as well as
  increasing computer capacity make possible to
  align a query sequence against a large DB in a
  few minutes.
• Two approaches for dynamic programming Top-down
  approach and Bottom-up.

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The procedure of the dynamic programming algorithm

• The alignment procedure depends upon scoring
  system based on probability that
• 1) a particular amino acid pair is found in
  alignments of related proteins (pxy)
• 2) the same amino acid pair is aligned by
  chance (pxpy)
• 3) introduction of a gap would be a better
  choice as it increases the score.
• A substitution matrix is composed of the ratio
  of the first two probabilities. There are many
  such matrices, two of them PAM and BLOSUM will be
  talked in next few slides.
• The calculation of scores for the gap
  introduction and its extension is from the
  matrices and represent a prior knowledge and some
  assumptions.
• For example one of them is quite simple, if
  negative cost of a gap is too high a reasonable
  alignment between slightly different sequences
  will be never achieved but if it is too low an
  optimal alignment is hardly possible. Other
  assumptions are based on sophisticated
  statistical procedures.

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An example scoring a sequence alignment with a
gap penalty
Sequence 1 V D S - C Y Sequence 2 V E S L
C Y Score 4 2 4 -11 9 7 Score
sum of amino acid pair scores (26) minus
single gap penalty (11) 15
Note 1. it is likely to have non-identical amino
acids placed in the corresponding positions.
2. Scores gained by each match are not
always the same, for instance two rare amino
acids will score more than two common.
3. The alignment gap(s) may be introduced for
optimising the score. Introduction of gaps causes
penalties.
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Steps for the dynamic programming algorithm

• Score of new Score of previous Score of
  new
• alignment alignment (A)
  aligned pair
• V D S - C Y V D S - C Y
• V E S L C Y V E S L C Y
• 15 8
  7
• 2. Score of Score of previous
  Score of new
• alignment (A) alignment (B)
  aligned pair
• V D S - C V D S - C
• V E S L C V E S L C
• 8 -1
  9
• 3. Repeat removing aligned pairs until end of
  alignments is reached

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Why use a substitution matrix?

• Determine likelihood of homology between two
  sequences.
• Substitutions that are more likely should get a
  higher score,
• Substitutions that are less likely should get a
  lower score.

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How to calculate Scoring Matrices

• Log-odds matrix where each cell gives the
  probability of aligning those two residues
• Score of alignment Sum of log-odds scores of
  residues
• Score for each residue given by

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Types of Matrices

• Percent Identity
• Standard scoring matrix to align DNA sequences
• PAM
• Estimates the rate at which each possible residue
  in a sequence changes to each other residue over
  time
• BLOSUM-X
• Identifies sequences that are X similar to the
  query sequence

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Scoring matrices PAM (Percent Accepted Mutation)
and BLOSUM62 (BLOcks amino acid SUbstitution
Matrices)
Amino acids are grouped according to to the
chemistry of the side group (C) sulfhydryl,
(STPAG)-small hydrophilic, (NDEQ) acid, acid
amide and hydrophilic, (HRK) basic, (MILV) small
hydrophobic, and (FYW) aromatic. Log odds values
10 means that ancestor probability is greater, 0
means that the probability are equal, -4 means
that the change is random. Thus the probability
of alignment YY/YY is 101020, whereas YY/TP is
3-5-8, a rare and unexpected between homologous
sequences.
BLOSUM is based on local alignments. BLOSUM was
first introduced in a paper by Henikoff and
Henikoff. They scanned the for very conserved
regions of protein families (that do not have
gaps in the sequence alignment) and then counted
the relative frequencies of amino acids and their
substitution probabilities. Then, they calculated
a log-odds score for each of the 210 possible
substitutions of the 20 standard amino acids.
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Word methods

• Word methods, also known as k-tuple methods, are
  heuristic methods that are not guaranteed to find
  an optimal alignment solution, but are
  significantly more efficient than dynamic
  programming.
• The typical tools used for this method is BLAST
  and FASTA.

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The list of sequence alignment software

• http//en.wikipedia.org/wiki/List_of_sequence_alig
  nment_software