Graphs and dna sequencing
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Graphs and DNA sequencing. CS 498 SS Saurabh Sinha. Eulerian Cycle Problem. Find a cycle that visits every edge exactly once Linear time. Find a cycle that visits every vertex exactly once NP – complete . Hamiltonian Cycle Problem. Game invented by Sir William Hamilton in 1857.

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Graphs and dna sequencing l.jpg

Graphs and DNA sequencing

CS 498 SS

Saurabh Sinha


Eulerian cycle problem l.jpg
Eulerian Cycle Problem

  • Find a cycle that visits everyedge exactly once

  • Linear time


Hamiltonian cycle problem l.jpg

Find a cycle that visits every vertex exactly once

NP – complete

Hamiltonian Cycle Problem

Game invented by Sir

William Hamilton in 1857



Viruses attack bacteria l.jpg
Viruses Attack Bacteria

  • Normally bacteriophage T4 kills bacteria

  • However if T4 is mutated (e.g., an important gene is deleted) it gets disabled and looses an ability to kill bacteria

  • Suppose the bacteria is infected with two different mutants each of which is disabled – would the bacteria still survive?

  • Amazingly, a pair of disabled viruses can kill a bacteria.

  • How can it be explained?


Benzer s experiment l.jpg
Benzer’s Experiment

  • Idea: infect bacteria with pairs of mutant T4 bacteriophage (virus)

  • Each T4 mutant has an unknown interval deleted from its genome

  • If the two intervals overlap: T4 pair is missing part of its genome and is disabled – bacteria survive

  • If the two intervals do not overlap: T4 pair has its entire genome and is enabled – bacteria die


Benzer s experiment and graphs l.jpg
Benzer’s Experiment and Graphs

  • Construct an interval graph: each T4 mutant is a vertex, place an edge between mutant pairs where bacteria survived (i.e., the deleted intervals in the pair of mutants overlap)

  • Interval graph structure reveals whether DNA is linear or branched DNA




Interval graph comparison l.jpg
Interval Graph: Comparison

Linear genome

Branched genome

See http://en.wikipedia.org/wiki/Interval_graph




Dna sequencing13 l.jpg
DNA Sequencing

  • Shear DNA into millions of small fragments

  • Read 500 – 700 nucleotides at a time from the small fragments (Sanger method)


Fragment assembly l.jpg
Fragment Assembly

  • Computational Challenge: assemble individual short fragments (reads) into a single genomic sequence (“superstring”)


Strategies for whole genome sequencing l.jpg
Strategies for whole-genome sequencing

  • Hierarchical – Clone-by-cloneyeast, worm, human

    • Break genome into many long fragments

    • Map each long fragment onto the genome

    • Sequence each fragment with shotgun

  • Whole Genome Shotgunfly, human, mouse, rat, fugu

    One large shotgun pass on the whole genome

    Until late 1990s the shotgun fragment assembly of human genome was viewed as intractable problem


Shortest superstring problem l.jpg
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



Shortest superstring problem18 l.jpg
Shortest Superstring Problem

  • Can be framed as Travelling Salesman Problem (TSP).

  • Early sequencing algorithms used a greedy approach: merge a pair of strings with maximum overlap first

    • Conjectured to have performance guarantee of 2.


A completely different sequencing method sequencing by hybridization l.jpg
A completely different sequencing method:Sequencing by Hybridization

  • 1988: SBH suggested as an an alternative sequencing method. Nobody believed it will ever work

  • 1991: Light directed polymer synthesis developed by Steve Fodor and colleagues.

  • 1994: Affymetrix develops first 64-kb DNA microarray

First microarray

prototype (1989)

First commercial

DNA microarray

prototype w/16,000

features (1994)

500,000 features

per chip (2002)


How sbh works l.jpg
How SBH Works

  • Attach all possible DNA probes of length l 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.


How sbh works cont d l.jpg
How SBH Works (cont’d)

  • Using a spectroscopic detector, determine which probes hybridize to the DNA fragment to obtain the l–mer composition of the target DNA fragment.

  • Apply a combinatorial algorithm to reconstruct the sequence of the target DNA fragment from the l– mer composition.


L mer composition l.jpg
l-mer composition

  • Spectrum ( s, l ) - 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


The sbh problem l.jpg
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

Different from the Shortest Superstring Problem


Sbh hamiltonian path approach l.jpg
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 eulerian path approach l.jpg

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 – mers from S


Euler theorem l.jpg
Euler Theorem

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

    in(v)=out(v)

  • Theorem: A connected graph is Eulerian (has an Eulerian cycle) if and only if each of its vertices is balanced.


Euler theorem proof l.jpg
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 l.jpg
Algorithm for Constructing an Eulerian Cycle

a. 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 l.jpg
Algorithm for Constructing an Eulerian Cycle (cont’d)

b. If cycle from (a) 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 d30 l.jpg
Algorithm for Constructing an Eulerian Cycle (cont’d)

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


Back to traditional sequencing l.jpg
Back to traditional sequencing

Shake

DNA fragments

Known

location

(restriction

site)

Vector

Circular genome

(bacterium, plasmid)

+

=



Read coverage l.jpg
Read Coverage

C

Length of genomic segment: L

Number of reads: n Coverage C = n l / L

Length of each read: l

How much coverage is enough?

Lander-Waterman model:

Assuming uniform distribution of reads, C=10 results in 1 gapped region per 1,000,000 nucleotides


Lander waterman model l.jpg
Lander-Waterman Model

  • Major Assumptions

    • Reads are randomly distributed in the genome

    • The number of times a base is sequenced follows a Poisson distribution

  • Implications

    • G= genome length, L=read length, N = # reads

    • Mean of Poisson: =LN/G (coverage)

    • % bases not sequenced: p(X=0) =0.0009 = 0.09%

    • Total gap length: p(X=0)*G

Average times

This model was used to plan the Human Genome Project…


Challenges in fragment assembly l.jpg

Repeat

Repeat

Repeat

Green and blue fragments are interchangeable when

assembling repetitive DNA

Challenges in Fragment Assembly

  • 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)


Repeat related problems in assembly l.jpg
Repeat-related problems in assembly

A

B

C

Overlap information (by comparing reads):

A,B;

B,C;

A,C

Shortest superstring: combine A & C !


Dealing with repeats l.jpg
Dealing with repeats

  • Approach 1: Break genome into large fragments (e.g., 150,000 bp long each), and sequence each separately.

  • The number of repeats comes down proportionately (e.g., 30,000 times for human genome)


Dealing with repeats38 l.jpg
Dealing with repeats

  • Approach 2: Use “mate-pair” reads

  • Fragments of length ~ L are selected, and both ends are sequenced

    • L >> length of typical repeat

  • Reads are now in pairs, separated by approximately known distance (L)

  • Both reads of a mate-pair are unlikely to lie in repeat regions

  • Using their approximate separation, we can resolve assembly problems


Shotgun sequencing l.jpg
Shotgun Sequencing

genomic segment

cut many times at random (Shotgun)

Get one or two reads from each segment

~500 bp

~500 bp


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