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Biological & Biomedical Sciences
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Math Problem
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# Bioinformatics: Gap Penalty, Genomic Alignment, Pairwise Sequence (Math Problem Sample)

Instructions:

QUESTION 1 (PAIRWISE ALIGNMENT)
QUESTION 2 (MULTIPLE SEQUENCE ALIGNMENT)
QUESTION 3 (PHYLOGENETICS)
QUESTION 4 (HOMOLOGY SEARCH)

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QUESTION 1 (PAIRWISE ALIGNMENT)
1 Types of pairwise sequence alignments are;
* Global alignment – this creates an end – to – end alignment of the sequence to be aligned.
* Genomic alignment – this emphasizes on DNA alignment while accounting for characteristics present in the genomic data.
* Local alignment – this one finds one or more alignments describing the most similar regions within the sequence to be aligned.
2 Linear gap penalty, is a gap penalty in which each inserted/deleted symbol in the gap contributes a constant negative score to the alignment, while affine gap penalty penalizes using insertion and deletion using a linear function in which one term is length independent. So, affine gap penalty combines both constant and linear gap penalties, that is why is the most preferable.
3 Gap penalty – 2, Match +3 and Mismatch – 1.
Step 1; we draw the (n+1) X (m+1) grid and populate the values CGGACAGT, CGAC. We will put a gap in the first box of the matrix, as follows:
T

G

A

C

A

G

G

C

C

G

A

C

Step 2; We fill in the table with the first value being a gap (0). Then we add the numbers starting from zero to the gap value. E.g. 0 +(-2)= - 2, -2+(-2)=-4 and so on.
T

-16

G

-14

A

-12

C

-10

A

-8

G

-6

G

-4

C

-2

0

-2

-4

-6

-8

C

G

A

C

Step 3: We fill in the rest of the table by adding; the box beside (+gap), the box at the bottom(+gap) and the diagonal box with the match and mismatch parameters. The we identify which one brings the higher score and indicate by an arrow which one brought the higher score, that’s what we indicate as follows;
T

-16

-692151670050017

11112517272000-154305-508000 19

13843015875000-167640-63500021

14478016192500-15303512700023

G

-14

-647701746250015

9461516002000-163830-50800017

-100330-508000 19

16827517589500-90805-50800021

A

-12

-7493015938500 13

193040-69850011493519177000-158115-1524000 15

10541017716500136525-298450017

-10477563500 19

C

-10

-717551555750011

10160017589500-130175-508000 13

10922015748000-97790-63500015

167640-234950016129016446500-13398569850017

A

-8

-67310149860009

10096516065500-130175-127000011

-117475-50800013

-124460-50800015

G

-6

8445516192500-17399019050009

1098551892300099695-2984500-1041406350011

14668516637000151765-2730500-126365-25400013

G

-4

-60960-3556000-61595158750005

-163830-571500 7

-117475-8890009

-128905190500 11

C

17589520129500-2

-163195-8255001

-166370-1206500-1

-1581153810000-17018019177000-3

0

-2

-4

-6

-8

C

G

A

C

Step 4; We want to make a trace back. Here, we need to find the highest value, usually the highest value is always at the last corner of the matrix. Ours is +23 (most suitable value). We trace it back to the starting point 0. So we require the arrows to know from which the value comes from until we get to 0. In our box, we will use a shading of red to show the traceback.
T

-16

-692151670050017

11112517272000-154305-508000 19

13843015875000-167640-63500021

14478016192500-15303512700023

G

-14

-647701746250015

9461516002000-163830-50800017

-100330-508000 19

16827517589500-90805-50800021

A

-12

-7493015938500 13

193040-69850011493519177000-158115-1524000 15

10541017716500136525-298450017

-10477563500 19

C

-10

-717551555750011

10160017589500-130175-508000 13

10922015748000-97790-63500015

167640-234950016129016446500-13398569850017

A

-8

-67310149860009

10096516065500-130175-127000011

-117475-50800013

-124460-50800015

G

-6

8445516192500-17399019050009

1098551892300099695-2984500-1041406350011

14668516637000151765-2730500-126365-25400013

G

-4

-60960-3556000-61595158750005

-163830-571500 7

-117475-8890009

-128905190500 11

C

17589520129500-2

-163195-8255001

-166370-1206500-1

-1581153810000-17018019177000-3

0

-2

-4

-6

-8

C

G

A

C

or
T

-16

-692151670050017

11112517272000-154305-508000 19

13843015875000-167640-63500021

14478016192500-15303512700023

G

-14

-647701746250015

9461516002000-163830-50800017

-100330-508000 19

16827517589500-90805-50800021

A

-12

-7493015938500 13

193040-69850011493519177000-158115-1524000 15

10541017716500136525-298450017

-10477563500 19

C

-10

-717551555750011

10160017589500-130175-508000 13

10922015748000-97790-63500015

167640-234950016129016446500-13398569850017

A

-8

-67310149860009

10096516065500-130175-127000011

-117475-50800013

-124460-50800015

G

-6

8445516192500-17399019050009

1098551892300099695-2984500-1041406350011

14668516637000151765-2730500-126365-25400013

G

-4

-60960-3556000-61595158750005

-163830-571500 7

-117475-8890009

-128905190500 11

C

17589520129500-2

-163195-8255001

-166370-1206500-1

-1581153810000-17018019177000-3

0

-2

-4

-6

-8

C

G

A

C

4 A local alignment on the sequences, we look for the trace back path (places shaded red)...
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