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 today’s 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 aa’s 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

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
Example:
SHALL WEAR
ALL WE
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 START IPTYGI STOP
START IPTYI STOP

20. Frame Shift
ATGATACCGACGTACGGCATTTAA
START IPTYGI STOP
START IPT STOP

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
Let’s 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 s[1..I] and t[1..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 s[1..i] with t[1..j - 1] and match a space with t[j]
Align s[1..i - 1] with t[1..j - 1] and match s[i] with t[j]
Align s[1..i - 1] with t[1..j] and match s[i] 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(s[1..i], t[1..j] = max {sim (s[1..i], t[1..j - 1]) - 2,
sim (s[1..i - 1], t[1..j - 1]) + p(i, j),
sim (s[1..i - 1], t[1..j]) - 2}
Where p(i, j) = + 1 if s[i] = t[j], -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
a[i, j] = max {a[i, j - 1] - 2,
a[i - 1, j - 1] + p(i, j),
a[i - 1, j] - 2}
Where p(i, j) = + 1 if s[i] = t[j], -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
a[i, 0]  i  g
for j  0 to n do
a[0, j]  j  g
for i  1 to m do
for j  1 to n do
a[i, j]  max(a[i - 1, j] + g,
a[i - 1, j - 1] + p(i, j), a[i, j - 1] + g)
return a[m, 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 …