Sequencing and Sequence Alignment

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Sequencing and Sequence Alignment

• CIS 667 Bioinformatics
• Spring 2004

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Protein Sequencing

• Before DNA sequencing, protein sequencing was
  common
• Sanger won a Nobel prize for determining amino
  acid sequence of insulin
• Protein sequences much shorter than todays DNA
  fragments
• One amino acid at a time can be removed from the
  protein
• The aa can then be determined

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Protein Sequencing

• Unfortunately, this works only for a few aas
  from the end
• So insulin broken up into fragments

Gly Ile Val Glu Ile Val Glu Gln Gln Cys Cys Ala
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Protein Sequencing

• Then the fragments are sequenced
• After they are assembled by finding the
  overlapping regions

Gly Ile Val Glu Ile Val Glu Gln
Gln Cys Cys Ala Gly Ile Val Glu Gln Cys Cys Ala
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Protein Sequencing

• By the late 1960s protein sequencing machines on
  market
• RNA sequencing following the same basic
  methodology by 1965

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DNA Sequencing

• DNA was first sequenced by transcribing DNA to
  RNA
• Slow - years to sequence tens of base pairs
• By mid 70s Maxam and Gilbert learned how to
  cleave DNA selectively at A, C, G, or T
• This led to the development of Maxam-Gilbert
  sequencing method

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Maxam-Gilbert Sequencing

• Single-stranded DNA labeled with radioactive tag
  at 5 end
• Sample quartered and digested in four
  base-specific reactions
• Reaction concentrations are such that each strand
  of DNA in each sample cut once at random location
• Use gel electrophoresis to find lengths of tagged
  fragments

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(No Transcript)
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Sanger Sequencing

• Today, an alternative method called Sanger
  sequencing is generally used
• A primer bonds to a single-stranded DNA near the
  3 end of the target to be sequenced
• DNA polymerase extends the primer along the
  target DNA
• For each of the 4 bases this extension is done

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Sanger Sequencing

• A small amount of extension ending nucleotides
  are introduced
• This causes the extension to end randomly at a
  specific base
• Now use gel electrophoresis and read the sequence
  as the complement of the bases

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Sanger Sequencing
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Sequence Alignment

• Given two string, find the optimal alignment of
  the strings
• Strings may be of different lengths, optimal
  alignment may include gaps
• An alignment score is produced

SHALL WEAR ALL WE
Example
SHALL WEAR --ALL WE--
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Sequence Alignment

• Alignment score produced by looking at each
  column in alignment
• Match gives column a 1 score
• Mismatch -1
• Space -2

HELLO THERE JELLO TEAR-
Score 7(1)3(-1)1(-2)2
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Sequence Alignment

• In biology, the sequences to be aligned consist
  of nucleotides or amino acids
• Sufficiently similar sequences can allow us to
  infer homology
• Common evolutionary history
• We can also infer the function of a protein or
  gene given similarity to one with known
  functionality

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Sequence Alignment

• Since homologous sequences share a common
  evolutionary history the alignment score should
  reflect evolutionary processes
• DNA changes over time due to mutations
• Most mutations are harmful
• May be due to environmental factors, e.g.
  radiation

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Mutation

• May also be due to problems in the transcription
  process
• One nucleotide may be substituted for another
• Deletion of a nucleotide
• Duplication
• Insertions
• Inversions

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Mutation
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Mutation

• Deletions have different effects depending on the
  number of nucleotides deleted
• Deletions of 3 in an ORF result in the deletion
  of a codon, so an amino acid is not produced
• Usually damaging, sometimes lethal
• Deletion of 1 causes a frame shift - changes all
  downstream amino acids
• Almost always lethal

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Codon Deletion
ATGATACCGACGTACGGCATTTAA
ATGATACCGACGTACGGCATTTAA
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Frame Shift
ATGATACCGACGTACGGCATTTAA
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Mutations

• Some notes
• A single base substitution may even produce the
  same amino acid (especially if it is the last in
  a codon)
• May also produce a similar amino acid
• It is impossible to tell whether the gap in an
  alignment results from insertion in one sequence
  or deletion from another
• After mutation, an organism may be more or less
  likely to survive natural selection

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Alignment Scores

• Based on what we have said about mutations - how
  should we modify the alignment scores?
• Note that a single long gap is more likely than
  several shorter ones
• Therefore it should have a smaller penalty
• Say
• Match 1
• Mismatch 0
• Gap origination -2
• Gap extension -1

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Alignment

• We can have sequences with different sizes
• An alignment is defined to be the insertion of
  spaces in arbitrary locations along the sequences
  so that they end up being the same size
• No space in the sequence can be aligned with a
  space in the other

GA-CGGATTAG GATCGGAATAG
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Alignment

• Lets use the following scores for similarity -
  match 1 mismatch -1 space -2
• Let sim(s, t) denote the similarity score for two
  sequences s and t
• We want to develop an algorithm to compute the
  maximum sim(s, t) given s and t

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Dynamic Programming

• We will use a technique known as dynamic
  programming
• Solve an instance of a problem by using an
  already solved smaller instance of the same
  problem
• In our case, we build up the solution by
  determining the similarities between arbitrary
  prefixes of the two sequences
• Start with shorter prefixes, work towards longer
  ones

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Dynamic Programming

• Let m be the size of s and n the size of t
• Then there are m 1 prefixes of s and n 1
  prefixes of t, including the empty string
• We store the similarities of the prefixes in an
  (m 1) ? (n 1) array
• Entry (I, j) contains the similarity between
  s1..I and t1..j

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Dynamic Programming

• Let s AAAC and t AGC
• We need to initialize part of the array to get
  started
• If one of the sequences is empty, we just add as
  many spaces as characters in the other sequence
• Correspondingly, we fill in the first row and
  column with multiples of the space penalty (-2)

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Dynamic Programming

• We can compute the value of entry (i, j) by
  looking at just three previous entries (i - 1,
  j), (i - 1, j - 1), (i, j - 1)
• Corresponds to these choices
• Align s1..i with t1..j - 1 and match a space
  with tj
• Align s1..i - 1 with t1..j - 1 and match
  si with tj
• Align s1..i - 1 with t1..j and match si
  with a space

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Dynamic Programming

• If we compute entries in an smart way, scores for
  best alignments between smaller prefixes have
  already been stored in the array, so

sim(s1..i, t1..j max sim (s1..i, t1..j
- 1) - 2, sim (s1..i - 1, t1..j - 1) p(i,
j), sim (s1..i - 1, t1..j) - 2 Where p(i, j)
1 if si tj, -1 otherwise
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Dynamic Programming

• We should fill in the array row by row, left to
  right
• If we denote the array by a then we have

ai, j max ai, j - 1 - 2, ai - 1, j - 1
p(i, j), ai - 1, j - 2 Where p(i, j) 1 if
si tj, -1 otherwise
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Dynamic Programming
Algorithm Similarity input sequences s and
t output similarity of s and t m ? s n ?
t for i ? 0 to m do ai, 0 ? i ? g for j ?
0 to n do a0, j ? j ? g for i ? 1 to m
do for j ? 1 to n do ai, j ? max(ai - 1,
j g, ai - 1, j - 1 p(i, j), ai, j - 1
g) return am, n
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Optimal Alignments

• So now we know the maximum similarity, but we
  still need to compute the optimal alignment
• We will use the array a of similarities
  previously computed
• To be continued