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Sequencing and Sequence Alignment

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Sequencing and Sequence Alignment CIS 667 Bioinformatics Spring 2004 Protein Sequencing Before DNA sequencing, protein sequencing was common Sanger won a Nobel prize ... – PowerPoint PPT presentation

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Title: Sequencing and Sequence Alignment


1
Sequencing and Sequence Alignment
  • CIS 667 Bioinformatics
  • Spring 2004

2
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

3
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
4
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
5
Protein Sequencing
  • By the late 1960s protein sequencing machines on
    market
  • RNA sequencing following the same basic
    methodology by 1965

6
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

7
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

8
(No Transcript)
9
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

10
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

11
Sanger Sequencing
12
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--
13
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
14
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

15
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

16
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

17
Mutation
18
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

19
Codon Deletion
ATGATACCGACGTACGGCATTTAA
ATGATACCGACGTACGGCATTTAA
20
Frame Shift
ATGATACCGACGTACGGCATTTAA
21
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

22
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

23
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
24
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

25
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

26
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

27
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)

28
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

29
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
30
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
31
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
32
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
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