Transcript Updated slides on graph algorithms for DNA sequencing

An Introduction to Bioinformatics Algorithms
www.bioalgorithms.info
Graph Algorithms
and Fragment Assembly
An Introduction to Bioinformatics Algorithms
www.bioalgorithms.info
Outline
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Introduction to Graph Theory
Eulerian & Hamiltonian Cycle Problems
Benzer Experiment and Interval Graphs
DNA Sequencing
The Shortest Superstring & Traveling
Salesman Problems
• Sequencing by Hybridization and de Bruijn
graphs
• Fragment Assembly and Repeats in DNA
• Fragment Assembly Algorithms
An Introduction to Bioinformatics Algorithms
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The Bridge Obsession Problem
Find a tour crossing every bridge just once
Leonhard Euler, 1735
Bridges of Königsberg
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Eulerian Cycle Problem
• Find a cycle that
visits every edge
exactly once
• Linear time
More complicated Königsberg
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Hamiltonian Cycle Problem
• Find a cycle that
visits every vertex
exactly once
• NP – complete
Game invented by Sir
William Hamilton in 1857
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Mapping Problems to Graphs
• Arthur Cayley studied
chemical structures
of hydrocarbons in
the mid-1800s
• He used trees
(acyclic connected
graphs) to enumerate
structural isomers
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Beginning of Graph Theory in Biology
Benzer’s work
• Developed deletion
mapping
• “Proved” linearity of
genomes
• Demonstrated
internal structure of
the genome
Seymour Benzer, 1950s
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Viruses Attack Bacteria
• Normally bacteriophage T4 (a virus) kills bacteria
• However if T4 is mutated (e.g., an important gene is
deleted) it gets disabled and loses 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 disable viruses can kill a
bacteria even if each of them is disabled.
• How can it be explained?
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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
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Complementation between pairs of
mutant T4 bacteriophages
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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 the
T4 DNA is linear or branched
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Interval Graph: Linear Genome
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Interval Graph: Branched Genome
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Interval Graph: Comparison
Linear genome
Branched genome
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DNA Sequencing: History
Sanger method (1977):
labeled ddNTPs
terminate DNA
copying at random
points.
Gilbert method (1977):
chemical method to
cleave DNA at specific
points (G, G+A, T+C, C).
Both methods generate
labeled fragments of
varying lengths that are
further electrophoresed.
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Sanger Method: Generating Read
1. Start at primer
(restriction site)
2. Grow DNA chain
3. Include ddNTPs
4. Stops reaction at all
possible points
5. Separate products
by length, using gel
electrophoresis
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DNA Sequencing (Shotgun)
• Shear DNA into
millions of small
fragments
• Read 500 – 700
nucleotides at a time
from the small
fragments (by e.g.
Sanger method)
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Fragment Assembly
• Computational Challenge: Assemble
individual short fragments (reads) into a
single genomic sequence (“superstring”)
• Until late 1990s the shotgun fragment
assembly of human genome was viewed as
an intractable problem
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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 – hard
• Note: this formulation does not take into
account sequencing errors
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Shortest Superstring Problem: Example
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Reducing SSP to TSP
• Define overlap( si, sj ) as the length of the longest (proper)
prefix of sj that matches a suffix of si.
aaaggcatcaaatctaaaggcatcaaa
aaaggcatcaaatctaaaggcatcaaa
What is overlap ( si, sj ) for these strings?
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Reducing SSP to TSP
• Define overlap( si, sj ) as the length of the longest (proper)
prefix of sj that matches a suffix of si.
aaaggcatcaaatctaaaggcatcaaa
aaaggcatcaaatctaaaggcatcaaa
aaaggcatcaaatctaaaggcatcaaa
overlap=12
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Reducing SSP to TSP
• Define overlap( si, sj ) as the length of the longest (proper)
prefix of sj that matches a suffix of si.
aaaggcatcaaatctaaaggcatcaaa
aaaggcatcaaatctaaaggcatcaaa
aaaggcatcaaatctaaaggcatcaaa
• Construct a complete graph with n vertices representing
the n strings s1, s2,…., sn.
• Insert edges of length overlap ( si, sj ) between vertices si
and sj.
• Find the longest path which visits every vertex exactly
once. This is the max Traveling Salesman Problem
(TSP), which is also NP – hard.
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Reducing SSP to TSP (cont’d)
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SSP to TSP: An Example
S = { ATC, CCA, CAG, TCC, AGT }
TSP
SSP
ATC
AGT
2
0
CCA
AGT
ATC
ATCCAGT
1
1
1
CCA
1
2
2
2
TCC
CAG
CAG
1
TCC
ATCCAGT
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Approximation Algorithms for SSP
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An Introduction to Bioinformatics Algorithms
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Sequencing by Hybridization (SBH): History
• 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)
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How SBH Works
• Attach all possible DNA probes (oligos) 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 (single strand) to the array.
• The DNA fragment hybridizes with those probes
that are complementary to substrings of length l
of the fragment.
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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 the combinatorial algorithm (below) to
reconstruct the sequence of the target DNA
fragment from the l–mer composition.
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Hybridization on DNA Array
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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
• 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}
An Introduction to Bioinformatics Algorithms
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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
• 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.
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Different sequences – the same spectrum
• Different sequences may have the same
spectrum:
Spectrum(GTATCT,2)=
Spectrum(GTCTAT,2)=
{AT, CT, GT, TA, TC}
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The SBH Problem
• Goal: Reconstruct a string from its l-mer
spectrum (which is a multiset)
• Input: A multiset S, representing all l-mers
from an (unknown) string s
• Output: String s such that Spectrum(s,l ) = S
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SBH: Hamiltonian Path Approach
S = { ATG AGG TGC TCC GTC GGT GCA CAG }
H
ATG
AGG
TGC
TCC
GTC
GGT
ATG CAGG TC C
Path visited every VERTEX once
GCA
CAG
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SBH: Hamiltonian Path Approach
A more complicated graph:
S = { ATG
H
TGG
TGC
GTG
GGC
GCA
GCG
CGT }
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SBH: Hamiltonian Path Approach
S = { ATG TGG
TGC
GTG
GGC GCA
GCG
CGT }
Path 1:
H
ATGCGTGGCA
Path 2:
H
ATGGCGTGCA
But the problem is NP-complete in general!
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SBH: Eulerian Path Approach
S = { ATG, TGG, 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. Multi-edges are allowed.
This data structure is now called a de Bruijn graph.
GT
AT
TG
CG
GC
GG
CA
Path visited every EDGE once
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SBH: Eulerian Path Approach
S = { ATG, TGG, TGC, GTG, GGC, GCA, GCG, CGT } may result in
two different paths:
GT
AT
TG
CG
GC
GG
ATGGCGTGCA
GT
CA
AT
TG
CG
GC
GG
ATGCGTGGCA
CA
An Introduction to Bioinformatics Algorithms
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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
(i.e., contains a Eulerian cycle) if and only if
each of its vertices is balanced.
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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
???
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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.
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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.
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Algorithm for Constructing an Eulerian Cycle (cont’d)
c. Combine the cycles
from (a) and (b) into
a single cycle and
iterate step (b).
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Euler Theorem: Extension
• Theorem: A connected graph has an
Eulerian path if and only if it contains two
(complementary) semi-balanced vertices and
all other vertices are balanced.
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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
• Practicality again: Although SBH is still impractical,
it spearheaded expression analysis and SNP
analysis techniques
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Traditional DNA Sequencing
DNA
Shake
DNA fragments
(clones)
Vector:
Circular genome
(bacterium, plasmid)
+
=
Insert
Known
location
(restriction
site)
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Different Types of Vectors
VECTOR
Size of insert (bp)
Plasmid
2,000 - 10,000
Cosmid
40,000
BAC (Bacterial Artificial
Chromosome)
70,000 - 300,000
YAC (Yeast Artificial
Chromosome)
> 300,000
Not used much
recently
A physcal map for
the clones is built,
and then each clone
is fragemented
again, sequenced
by Sanger method,
and assembled.
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Electrophoresis Diagrams
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Challenging to Read Answer
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Reading an Electropherogram
• Filtering
• Smoothening
• Correction for length compressions
• A method for calling the nucleotides – PHRED
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Shotgun Sequencing
genomic segment
cut many times at
random (Shotgun)
Get one or two reads
(double barreled) from
each fragment
~500 bp
~500 bp
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Fragment Assembly
reads
Cover region with ~7-fold redundancy
Overlap reads and extend to reconstruct the
original genomic region
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Read Coverage
C
Length of genomic segment: L
Number of (sequenced) reads: n
Length of each read: l
Coverage
C=nl/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
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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
Repeat
Repeat
Green and blue fragments are interchangeable when
assembling repetitive DNA
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Repeat Types
•
Low-complexity DNA (e.g., ATATATATACATA…)
•
Microsatellite repeats
•
Transposons/retrotransposons
• SINE
Short Interspersed Nuclear Elements
(e.g., Alu: ~300 bps long, 106 copies)
(a1…ak)N where k ~ 3-6 bps
(e.g., CAGCAGTAGCAGCACCAG)
•
LINE
Long Interspersed Nuclear Elements
~500 - 5,000 bps long, 200,000 copies
•
LTR retroposons
•
Gene families
Long Terminal Repeats (~700 bps) at
each end
genes duplicate & then diverge
•
Segmental duplications
~very long, very similar copies
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Overlap-Layout-Consensus
Assemblers: ARACHNE, PHRAP, CAP, TIGR, CELERA
Overlap: find potentially overlapping reads
Layout: merge reads into contigs and
contigs into supercontigs (scaffolds)
Consensus: derive the DNA sequence
and correct sequencing errors
..ACGATTACAATAGGTT..
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Overlap
• Find the best match between some suffix of
one read and some prefix of another
• Due to sequencing errors, we need to use
dynamic programming to find the optimal
overlap alignment
• Apply a fast filtration method to filter out pairs
of reads that do not share a significantly long
common substring
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Overlapping Reads
•
Sort all k-mers in reads
•
Find pairs of reads sharing a k-mer
•
(k ~ 24)
Extend to full alignment – throw away if not
>95% similar
TACA TAGATTACACAGATTAC T GA
|| ||||||||||||||||| | ||
TAGT TAGATTACACAGATTAC TAGA
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Overlapping Reads and Repeats
• A k-mer that appears N times initiates N2
comparisons
• For an Alu that appears 106 times  1012
comparisons – too much
• Solution:
Discard all k-mers that appear more than
t  Coverage (e.g., t ~ 10)
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From Overlapping Reads to Layout
Create local multiple alignments from the
overlapping reads
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
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Layout
• Repeats are a major challenge
• Do two aligned fragments really overlap, or are
they from two copies of a repeat?
• Solution: repeat masking – hide the repeats!!!
• But masking results in a high rate of
misassembly (up to 20%)
• Misassembly means alot more work at the
finishing step
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Merge Reads into Contigs
repeat region
Merge reads up to potential repeat boundaries
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Repeats, Errors and Read Lengths
• Repeats shorter than read length are OK
• Repeats with more base pair differences than
sequencing error rate are OK
• To make a smaller portion of the genome
appear repetitive, try to:
• Increase read length
• Decrease sequencing error rate
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Link Contigs into Supercontigs
Normal density
Too dense:
Overcollapsed?
Inconsistent links:
Overcollapsed?
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Consensus
• A consensus sequence is derived from a
profile of the assembled fragments
• A sufficient number of reads is required to
ensure a statistically significant consensus
• Reading errors are corrected
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Derive Consensus Sequence
TAGATTACACAGATTACTGA TTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAAACTA
TAG TTACACAGATTATTGACTTCATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGGGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
Derive multiple alignment from pairwise read alignments
(i.e., progressive alignment)
Derive each consensus base by weighted voting
Another approach based on finding a longest path in a
DAG is given in the popular assembler Phrap
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EULER – Yet Another Approach to
Fragment Assembly
• Traditional “overlap-layout-consensus” technique
has a high rate of mis-assembly
• EULER uses the Eulerian Path approach borrowed
from the SBH problem and a de Bruijn graph
constructed from k-mers
• Fragment assembly without repeat masking can be
done in linear time with a greater accuracy. The
approach is popular among NGS assemblers.
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Multiple Repeats
Repeat1
Repeat2
Repeat1
Repeat2
Can be easily
constructed with any
number of repeats
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Construction of Repeat Graph
• Construction of repeat graph from k – mers:
emulates an SBH experiment with a huge
(virtual) DNA chip.
• Breaking reads into k – mers: Transform
sequencing data into virtual DNA chip data.
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Construction of Repeat Graph (cont’d)
• Error correction in reads: “consensus first”
approach to fragment assembly. Makes
reads (almost) error-free BEFORE the
assembly even starts.
• Using reads and mate-pairs to simplify the
repeat graph (Eulerian Superpath Problem).
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Approaches to Fragment Assembly
Find a path visiting every VERTEX exactly
once in the OVERLAP graph:
Hamiltonian path problem
NP-complete: algorithms unknown
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Approaches to Fragment Assembly
(cont’d)
Find a path visiting every EDGE exactly once
in the REPEAT graph:
Eulerian path problem
Linear time algorithms are known
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Making Repeat Graph Without DNA
• Problem: Construct the repeat graph from a
collection of reads.
?
• Solution: Break the reads into smaller pieces.
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Repeat Sequences: Emulating a
DNA Chip
• Virtual DNA chip allows the biological
problem to be solved within the technological
constraints.
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Repeat Sequences: Emulating a
DNA Chip (cont’d)
• Reads are constructed from an original
sequence in lengths that allow biologists a
high level of certainty.
• They are then broken again to allow the
technology to sequence each within a
reasonable array.
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Minimizing Errors
• If an error exists in one of the 20-mer reads,
the error will be perpetuated among all of the
smaller pieces broken from that read.
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Minimizing Errors (cont’d)
• However, that error will not be present in the
other instances of the 20-mer read.
• So it is possible to eliminate most point
mutation errors before reconstructing the
original sequence.
An Introduction to Bioinformatics Algorithms
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Conclusions
• Graph theory is a vital tool for solving
biological problems
• Wide range of applications, including
sequencing, motif finding, protein networks,
and many more
An Introduction to Bioinformatics Algorithms
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References
• Simons, Robert W. Advanced Molecular Genetics Course, UCLA
(2002).
http://www.mimg.ucla.edu/bobs/C159/Presentations/Benzer.pdf
• Batzoglou, S. Computational Genomics Course, Stanford
University (2004).
http://www.stanford.edu/class/cs262/handouts.html