Ch04 dna mapping

DNA Mapping and Brute

Force Algorithms

1. Restriction Enzymes

2. Gel Electrophoresis

3. Partial Digest Problem

4. Brute Force Algorithm for Partial Digest Problem

5. Branch and Bound Algorithm for Partial Digest Problem

6. Double Digest Problem

Outline

Section 1:

Restriction Enzymes

Discovery of Restriction Enzymes

• HindII: First restriction enzyme.

• Was discovered accidentally in 1970 while scientists were

studying how the bacterium Haemophilus influenzae takes

up DNA from the virus.

• Recognizes and cuts DNA at sequences:

• GTGCAC

• GTTAAC

Discovering Restriction Enzymes

Werner Arber

My father has discovered a

servant who serves as a pair of

scissors. If a foreign king invades

a bacterium, this servant can cut

him in small fragments, but he

does not do any harm to his own

king. Clever people use the

servant with the scissors to find

out the secrets of the kings. For

this reason my father received

the Nobel Prize for the discovery

of the servant with the scissors.”

Daniel Nathans’ daughter

(from Nobel lecture)

Hamilton SmithDaniel Nathans

Werner Arber – discovered restriction

enzymes

Daniel Nathans - pioneered the application

of restriction for the

construction of genetic

Hamilton Smith - showed that restriction

enzyme cuts DNA in the

middle of a specific sequence

Molecular Cell Biology, 4thEdition

Molecular Scissors

Restriction Enzymes: Common Recognition Sites

Molecular Cell Biology, 4thEdition

• Recombinant DNA technology

• Cloning

• cDNA/genomic library construction

• DNA mapping

Uses of Restriction Enzymes

Recombinant DNA technology

cDNA Library

• A restriction map is a map showing positions of restriction sites

in a DNA sequence.

• If DNA sequence is known then construction of restriction map

is trivial exercise.

• In early days of molecular

biology DNA sequences

were often unknown.

• Biologists had to solve

the problem of constructing

restriction maps without

knowing DNA sequences.

Restriction Maps

Full Restriction Digest

• Cutting DNA at each restriction site creates multiple

restriction fragments:

• Full Restriction Digest: Is it possible to reconstruct the order

of the fragments from the sizes of the fragments?

• Example: Say the fragments have lengths {3,5,5,9} as in

the above sequence.

Full Restriction Digest: Multiple Solutions

• For the set of fragment lengths {3, 5, 5, 9} we have the

original segment as a possible solution:

• However, we could also have the following segment:

Section 2:

Gel Electrophoresis

• Restriction enzymes break DNA into restriction fragments.

• Gel electrophoresis:A process for separating DNA by size

and measuring sizes of restriction fragments.

• Modern electrophoresis machines can separate DNA

fragments that differ in length by 1 nucleotide for fragments

up to 500 nucleotides long.

Gel Electrophoresis: Measure Segment Lengths

• DNA fragments are injected into a gel positioned in an

electric field.

• DNA are negatively charged near neutral pH.

• The ribose phosphate backbone of each nucleotide is

acidic; DNA has an overall negative charge.

• Thus DNA molecules move towards the positive electrode.

Gel Electrophoresis: How It Works

Gel Electrophoresis

• DNA fragments of different lengths are separated according to

• Smaller molecules move through the gel matrix more readily

than larger molecules.

• The gel matrix restricts random diffusion so molecules of

different lengths separate into different bands.

Gel Electrophoresis

Direction of DNA

movement

Detecting DNA: Autoradiography

• Separated DNA bands on a

gel can be viewed via

autoradiography:

1. DNA is radioactively

labeled.

2. The gel is laid against a

sheet of photographic

film in the dark,

exposing the film at the

positions where the

DNA is present.

Molecular Cell Biology, 4th edition

• Another way to visualize DNA bands in gel is through

fluorescence:

• The gel is incubated with a solution containing the

fluorescent dye ethidium.

• Ethidium binds to the DNA.

• The DNA lights up when the gel is exposed to ultraviolet

light.

Detecting DNA: Fluorescence

Section 3:

Partial Digest Problem

• The sample of DNA is exposed to the restriction enzyme for

only a limited amount of time to prevent it from being cut at

all restriction sites; this procedure is called partial

(restriction) digest.

• This experiment generates the set of all possible restriction

fragments between every two (not necessarily consecutive)

• This set of fragment sizes is used to determine the positions of

the restriction sites in the DNA sequence.

Partial Restriction Digest

• Partial Digest results in the following 10 restriction fragments:

Partial Digest: Example

• We assume that multiplicity of a fragment can be detected, i.e.,

the number of restriction fragments of the same length can be

determined.

• Here we would detect two

fragments of length 5 and

two of length 14.

Multiset: {3, 5, 5, 8, 9, 14, 14, 17, 19, 22}

• We therefore have a multiset of fragment lengths.

• We now provide a basic mathematical framework for the

partial digest process.

The multiset of integers representing lengths of each of the

DNA fragments produced from a partial digest; formed

from X by taking all pairwise differences.

The set of n integers representing the location of all cuts in

the restriction map, including the start and end.

Partial Digest: Mathematical Framework

Return to Partial Digest Example

0 5 14 19 22

• n = 5

0 5 14 19 22

• n = 5

• X = {0, 5, 14, 19, 22}

0 5 14 19 22

• n = 5

• X = {0, 5, 14, 19, 22}

• DX = {3,5,5,8,9,14,14,17,19,22}

• n = 5

• X = {0, 5, 14, 19, 22}

• DX = {3,5,5,8,9,14,14,17,19,22}

• Represent DX as a table, with

elements of X along both the top and

left sides.

• n = 5

• X = {0, 5, 14, 19, 22}

• DX = {3,5,5,8,9,14,14,17,19,22}

elements of X along both the top

and left sides.

X 0 5 14 19 22

• n = 5

• X = {0, 5, 14, 19, 22}

• DX = {3,5,5,8,9,14,14,17,19,22}

elements of X along both the top

and left sides.

• We place xj – xi into entry (i,j) for all

1 ≤ i<j ≤ n

X 0 5 14 19 22

0 5 14 19 22

5 9 14 17

14 5 8

• Goal: Given all pairwise distances between points on a line,

reconstruct the positions of those points.

• Input: The multiset of pairwise distances L, containing n(n-

1)/2 integers.

• Output: A set X, of n integers, such that ∆X = L.

Partial Digest Problem (PDP): Formulation

• It is not always possible to uniquely reconstruct a set X based

only on ∆X.

• Example:The sets

X = {0, 2, 5} (X + 10) = {10, 12, 15}

both produce ∆X = ∆(X + 10) ={2, 3, 5} as their partial

digest.

• Two sets X and Y are homometric if ∆X = ∆Y.

• The sets {0,1,2,5,7,9,12} and {0,1,5,7,8,10,12} present a less

trivial example of homometric sets. They both digest into:

{1, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8, 9, 10, 11, 12}

Multiple Solutions to the PDP

X = {0,1,2,5,7,9,12}

Homometric Sets: Example

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

X = {0,1,2,5,7,9,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

0 1 2 5 7 9 12

1 1 4 6 8 11

2 3 5 7 10

5 2 4 7

0 1 5 7 8 10 12

1 4 6 7 9 11

5 2 3 5 7

7 1 3 5

X = {0,1,2,5,7,9,12} Y = {0,1,5,7,8,10,12}

Section 4:

Brute Force Algorithm for

Partial Digest Problem

• Brute force algorithms, also known as exhaustive search

algorithms, examine every possible variant to find a solution.

• Efficient only in rare cases; usually impractical.

Brute Force Algorithms

1. Find the restriction fragment of maximum length M. Note: M

is the length of the DNA sequence.

2. For every possible set X={0, x2, … ,xn-1, M}

compute the corresponding DX.

3. If DX is equal to the experimental partial digest L, then X is

a possible restriction map.

Partial Digest: Brute Force

1. AnotherBruteForcePDP(L, n)

2. Mmaximum element in L

3. for every set of n – 2 integers 0 < x2 < … xn-1 < M

4. X{ 0,x2,…,xn-1,M }

5. Form DX from X

6. if DX = L

7. return X

8. output “no solution”

• BruteForcePDP takes O(M n-2) time since it must examine all

possible sets of positions. Note: the number of such sets is

• One way to improve the algorithm is to limit the values of xi

to only those values which occur in L, because we are

assuming for the sake of simplicity that 0 is contained in X.

)()2()2)(1()!2(

)!1()!2(

MOnMMMnnMn

Efficiency of BruteForcePDP

Another BruteForcePDP

• Limiting the members of X to those contained in L is almost

identical to BruteForcePDP, except for line 3:

2. M maximum element in L

3. for every set of n – 2 integers 0 < x2 < … xn-1 < M

4. X { 0,x2,…,xn-1,M }

5. Form DX from X

6. if DX = L

7. return X

Another BruteForcePDP

• Limiting the members of X to those contained in L is almost

identical to BruteForcePDP, except for line 3:

2. M maximum element in L

3. for every set of n – 2 integers 0 < x2 < … xn-1 < M from L

4. X { 0,x2,…,xn-1,M }

5. Form DX from X

6. if DX = L

7. return X

• More efficient than BruteForce PDP, but still slow.

• If L = {2, 998, 1000} (n = 3, M= 1000), BruteForcePDP will

be extremely slow, but AnotherBruteForcePDP will be quite

• Fewer sets are examined, but runtime is still exponential:

O(n2n-4).

Another BruteForcePDP: Efficiency

Section 5:

Branch and Bound

Algorithm for Partial

Digest Problem

1. Begin with X = {0}.

Branch and Bound Algorithm for PDP

2. Remove the largest element in L and place it in X.

3. See if the element fits on the right or left side of the restriction

4. When if fits, find the other lengths it creates and remove those

from L.

5. Go back to step 1 until L is empty.

from L.

5. Go back to step 1 until L is empty.

WRONG ALGORITHM

• Before describing PartialDigest, first define D(y, X) as the

multiset of all distances between point y and all other points in

the set X.

xyxyxyXyD

,,,for

,,,),(

Defining D(y, X)

return 15

to),( lengths add and from Remove 14

),PLACE( 13

from ),( lengths remove and to Add 12

),( if 11

\ to),( lengths add and from Remove 10

),PLACE( 9

from ),( lengths remove and to Add 8

),( if 7

),DELETE( 6

in element maximumy 5

return 4

output 3

empty is if 2

),PLACE( 1

LXywidthDXywidth

LXywidthD

LXyDXy

),PLACE( 4

),DELETE( 2

in element Maximum 1

)est(PartialDig

Lwidth

Simply deletes width from L

PartialDigest: Pseudocode