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DNA Sequencing. (Lecture for CS498-CXZ Algorithms in Bioinformatics) Sept. 8, 2005 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Many slides are taken/adapted from http://www.bioalgorithms.info/slides.htm. Outline.

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dna sequencing

DNA Sequencing

(Lecture for CS498-CXZ Algorithms in Bioinformatics)

Sept. 8, 2005

ChengXiang Zhai

Department of Computer Science

University of Illinois, Urbana-Champaign

Many slides are taken/adapted from http://www.bioalgorithms.info/slides.htm

outline
Outline
  • The Basic Shotgun Sequencing Strategy
  • The shortest superstring problem
    • Graph algorithms
  • Sequencing by Hybridization
the basic shotgun sequencing strategy step 1 fragment sequencing
The Basic Shotgun Sequencing StrategyStep 1: Fragment Sequencing

genomic segment

cut many times at random (Shotgun)

Get one or two reads from each segment

~500 bp

~500 bp

slide4

The Basic Shotgun Sequencing StrategyStep 2: Fragment Assembly

reads

Cover region with ~7-fold redundancy

Overlap reads and extend to reconstruct the original genomic region

step 1 generating read sanger method
Start at primer (restriction site)

Grow DNA chain

Include ddNTPs

Stops reaction at all possible points

Separate products by length, using gel electrophoresis

TAA ... T …T

Step 1: Generating Read (Sanger Method)
step 2 shortest superstring problem
Step 2: Shortest Superstring Problem
  • Problem: Given a set of strings, find a shortest string that contains all of them
  • Input: Strings s1, s2,…., sn
  • Output: A string s that contains all strings

s1, s2,…., sn as substrings, such that the length of s is minimized

  • Complexity: NP – complete

How likely is the found s indeed the original genome?

s approaches the genome as n if no sequencing error and fragmentation is random

shortest superstring problem example
Shortest Superstring Problem: Example

How do we solve such a problem efficiently?

- Greedy algorithms (approximation)

- Efficient algorithms exist for special

cases and are related to “graph

algorithms”

a greedy algorithm for ssp
A Greedy Algorithm for SSP
  • For each pair of (segment) strings, compute an overlap score
  • Merge the pair with the highest score
  • Repeat until no more strings can be merged
  • If multiple strings are left, any concatenation of them would be a solution.

Think about an example when this algorithm is not optimal…

a special case of ssp
A Special Case of SSP
  • When each segment is an L-mer (L-gram), linear algorithm exists!
  • This makes it attractive to do “sequencing by hybridization”…
sequencing by hybridization
Sequencing By Hybridization
  • Attach all possible DNA probes of length l (e.g.,

l = 8) to a flat surface, each probe at a distinct and known location. This set of probes is called the DNA array.

  • Apply a solution containing fluorescently labeled DNA fragment to the array.
  • The DNA fragment hybridizes with those probes that are complementary to substrings of length l of the fragment, allowing us to see which l-mers match the DNA fragment
l mer composition
l-mer composition
  • Define Spectrum ( s, l ) as the unordered multiset of all possible

(n – l + 1) l-mers in a string s of length n

  • The order of individual elements in Spectrum ( s, l ) does not matter
l mer composition1
l-mer composition
  • For example, for s = TATGGTGC all of the following are equivalent representations of Spectrum ( s, 3 ):

{TAT, ATG, TGG, GGT, GTG, TGC}

{ATG, GGT, GTG, TAT, TGC, TGG}

{TGG, TGC, TAT, GTG, GGT, ATG}

l mer composition2
l-mer composition
  • For example, for s = TATGGTGC all of the following are equivalent representations of Spectrum ( s, 3 ):

{TAT, ATG, TGG, GGT, GTG, TGC}

{ATG, GGT, GTG, TAT, TGC, TGG}

{TGG, TGC, TAT, GTG, GGT, ATG}

We usually choose the lexicographically maximal representation as the canonical one.

different sequences the same spectrum
Different sequences – the same spectrum
  • Different sequences may have the same spectrum:

Spectrum(GTATCT,2)=

Spectrum(GTCTAT,2)=

{AT, CT, GT, TA, TC}

the sbh problem
The SBH Problem
  • Goal: Reconstruct a string from its l-mer composition
  • Input: A set S, representing all l-mers from an (unknown) string s
  • Output: String s such that Spectrum ( s,l ) = S

and the length of s is minimum

How likely is the found s indeed the original genome?

s approaches the genome as l if no sequencing error

how do we solve the sbh problem efficiently

How do we solve the SBH problem efficiently?

The solution is related to graph algorithms…

the bridge obsession problem
The Bridge Obsession Problem

Find a tour crossing every bridge just once

Leonhard Euler, 1735

Bridges of Königsberg

formalization of k nigsberg bridge problem graph eulerian cycle
Formalization of Königsberg Bridge Problem: Graph & Eulerian Cycle
  • Graph G=(V,E)
    • V= Vertices; E= Edges
  • Eulerian cycle: A cycle that visits everyedge exactly once
  • Linear time algorithm exists

More complicated Königsberg

hamiltonian cycle problem
Find a cycle that visits every vertex exactly once

NP – complete

Hamiltonian Cycle Problem

Game invented by Sir

William Hamilton in 1857

balanced graphs
Balanced Graphs
  • A graph is balanced if for every vertex the number of incoming edges equals to the number of outgoing vertices:

in(v)=out(v)

euler theorem
Euler Theorem
  • A graph is balanced if for every vertex the number of incoming edges equals to the number of outgoing vertices:

in(v)=out(v)

  • Theorem: A connected graph is Eulerian if and only if each of its vertices is balanced.
euler theorem proof
Euler Theorem: Proof
  • Eulerian  balanced

for every edge entering v (incoming edge) there exists an edge leaving v (outgoing edge). Therefore

in(v)=out(v)

  • balanced  Eulerian

???

algorithm for constructing an eulerian cycle
Algorithm for Constructing an Eulerian Cycle
  • Start with an arbitrary vertex v and form an arbitrary cycle with unused edges until a dead end is reached. Since the graph is Eulerian this dead end is necessarily the starting point, i.e., vertex v.
algorithm for constructing an eulerian cycle cont d
Algorithm for Constructing an Eulerian Cycle (cont’d)

b. If cycle from (a) above is not an Eulerian cycle, it must contain a vertex w, which has untraversed edges. Perform step (a) again, using vertex w as the starting point. Once again, we will end up in the starting vertex w.

algorithm for constructing an eulerian cycle cont d1
Algorithm for Constructing an Eulerian Cycle (cont’d)

c. Combine the cycles from (a) and (b) into a single cycle and iterate step (b).

euler theorem extension
Euler Theorem: Extension
  • Theorem: A connected graph has an Eulerian path if and only if it contains at most two semi-balanced vertices and all other vertices are balanced.
reducing ssp to tsp traveling salesman problem
Reducing SSP to TSP (Traveling Salesman Problem)
  • Define overlap ( si, sj ) as the length of the longest prefix of sj that matches a suffix of si.
  • Construct a graph with n vertices representing the n strings s1, s2,…., sn. Insert edges of length overlap ( si, sj ) between vertices siand sj.
  • Find the shortest path which visits every vertex exactly once. This is the Traveling Salesman Problem (TSP), which is also NP – complete.
ssp to tsp an example
S = { ATC, CCA, CAG, TCC, AGT }

SSP

AGT

CCA

ATC

ATCCAGT

TCC

CAG

SSP to TSP: An Example

TSP

ATC

2

0

1

1

AGT

1

CCA

1

2

2

2

1

TCC

CAG

ATCCAGT

sbh hamiltonian path approach
SBH: Hamiltonian Path Approach

S = { ATG AGG TGC TCC GTC GGT GCA CAG }

H

ATG

AGG

TGC

TCC

GTC

GCA

CAG

GGT

ATG

C

A

G

G

T

C

C

Path visited every VERTEX once

sbh hamiltonian path approach1
A more complicated graph:

S = { ATG TGG TGC GTG GGC GCA GCG CGT }

SBH: Hamiltonian Path Approach
sbh hamiltonian path approach2
S = { ATG TGG TGC GTG GGC GCA GCG CGT }

Path 1:

SBH: Hamiltonian Path Approach

ATGCGTGGCA

Path 2:

ATGGCGTGCA

sbh eulerian path approach

CG

GT

TG

CA

AT

GC

Path visited every EDGE once

GG

SBH: Eulerian Path Approach

S = { ATG, TGC, GTG, GGC, GCA, GCG, CGT }

Vertices correspond to ( l – 1 ) – mers : { AT, TG, GC, GG, GT, CA, CG }

Edges correspond to l – tuples from S

sbh eulerian path approach1
S = { AT, TG, GC, GG, GT, CA, CG } corresponds to two different paths:SBH: Eulerian Path Approach

CG

CG

GT

GT

TG

AT

TG

GC

AT

GC

CA

CA

GG

GG

ATGGCGTGCA

ATGCGTGGCA

some difficulties with sbh
Some Difficulties with SBH
  • Fidelity of Hybridization: difficult to detect differences between probes hybridized with perfect matches and 1 or 2 mismatches
  • Array Size: Effect of low fidelity can be decreased with longer l-mers, but array size increases exponentially in l. Array size is limited with current technology.
  • Practicality: SBH is still impractical. As DNA microarray technology improves, SBH may become practical in the future
the problem of repeats

Repeat

Repeat

Repeat

Green and blue fragments are interchangeable when

assembling repetitive DNA

The Problem of Repeats
  • Repeats: A major problem for fragment assembly
  • > 50% of human genome are repeats:

- over 1 million Alu repeats (about 300 bp)

- about 200,000 LINE repeats (1000 bp and longer)

We’ll talk about how to deal with this in the next lecture….

what you should know
What You Should Know
  • The basic idea of shotgun sequencing
  • The shortest superstring problem formulation and its limitation
  • The reduction of the shortest superstring problem to graph traversal problems
  • Eulerian path algorithm
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