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Sequence comparison: Local alignment

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Local alignment algorithm: Smith-Waterman. A simple example. 2 -7 -5 -7. T -7. 2 -7 -5 ... Smith-Waterman local alignment algorithm: No score is negative. ... – PowerPoint PPT presentation

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Title: Sequence comparison: Local alignment


1
Sequence comparison Local alignment
  • Genome 559 Introduction to Statistical and
    Computational Genomics
  • Prof. William Stafford Noble

2
One-minute responses
  • It would be helpful to somehow get the solutions
    for the sample problems in our lecture printouts.
  • You can see the solutions by visiting the class
    web page and opening the slides there.
  • I am still a little confused about the difference
    between strings and lists.
  • A string is like a tuple of characters. Unlike a
    string a list is (1) mutable and (2) may contain
    other objects besides characters.
  • The Biotechniques paper went into a lot of detail
    -- how much of this should we understand?
  • I intend the paper to provide background for
    those who are interested. You should be sure you
    understand just what I go over in lecture.
  • I am slightly worried because I never seem to do
    things in the most straightforward way.
  • This just takes practice. Often, there is no
    single best way.

3
One-minute responses
  • There was perhaps a bit too much programming in
    this class.
  • There was more class time for Python, which was
    nice.
  • I really liked the sample problem times.
  • Problem set is very reasonable.
  • The examples and practice are most useful
    teaching methods for me at least. I am getting
    comfortable with the code through practice.
  • I like the sample problems. In the last few
    classes I felt rushed to finish them, but this
    time I was able to do all 3. It's very
    satisfying when they work.
  • I had somewhat more difficulty with today's
    exercises. I think it was due to the inherent
    complexity of adding new types to the repertoire.
  • Class moved at a good speed today.
  • I enjoyed the pace today.
  • Today's pace was good.
  • The pace was good -- it was helpful for me to
    have more time for problems.
  • Good pace.
  • Programming problems were a good speed today.
  • The biostats portion was a little fast but
    manageable.

4
One-minute responses
  • The cheat sheet really helped.
  • I really liked the list of operations and methods
    on the back of the lecture notes.
  • Lists of commands in slides were helpful.
  • Reviewing the DP matrix was very helpful.
  • I'm glad we reviewed the Needleman-Wunsch
    algorithm.
  • The traceback review helped me realize I'd
    forgotten how to do it.

5
Local alignment
  • A single-domain protein may be homologous to a
    region within a multi-domain protein.
  • Usually, an alignment that spans the complete
    length of both sequences is not required.

6
BLAST allows local alignments
Global alignment
Local alignment
7
Global alignment DP
  • Align sequence x and y.
  • F is the DP matrix s is the substitution matrix
    d is the linear gap penalty.

8
Local alignment DP
  • Align sequence x and y.
  • F is the DP matrix s is the substitution matrix
    d is the linear gap penalty.

9
Local DP in equation form
0
10
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G

A
G
C
0
11
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0
G 0
C 0
0
12
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0
G 0
C 0
0
-5
2
0
-5
0
13
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0 2
G 0 ?
C 0 ?
0
14
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0 2 ? ?
G 0 0 ? ?
C 0 0 ? ?
0
15
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0 2 2 0
G 0 0 0 4
C 0 0 0 0
0
16
Local alignment
  • Two differences with respect to global alignment
  • No score is negative.
  • Traceback begins at the highest score in the
    matrix and continues until you reach 0.
  • Global alignment algorithm Needleman-Wunsch.
  • Local alignment algorithm Smith-Waterman.

17
A simple example
Find the optimal local alignment of AAG and
AGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
A 0 2 2 0
G 0 0 0 4
C 0 0 0 0
0
AG AG
18
Local alignment
Find the optimal local alignment of AAG and
GAAGGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
G 0
A 0
A 0
G 0
G 0
C 0
0
19
Local alignment
Find the optimal local alignment of AAG and
GAAGGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
G 0 0 0 2
A 0 2 2 0
A 0 2 4 0
G 0 0 0 6
G 0 0 0 2
C 0 0 0 0
0
20
Local alignment
Find the optimal local alignment of AAG and
GAAGGC. Use a gap penalty of d-5.
A C G T
A 2 -7 -5 -7
C -7 2 -7 -5
G -5 -7 2 -7
T -7 -5 -7 2
A A G
0 0 0 0
G 0 0 0 2
A 0 2 2 0
A 0 2 4 0
G 0 0 0 6
G 0 0 0 2
C 0 0 0 0
AAG AAG
0
21
Summary
  • Local alignment finds the best match between
    subsequences.
  • Smith-Waterman local alignment algorithm
  • No score is negative.
  • Trace back from the largest score in the matrix.
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