1 Sequencing and Sequence Assembly --overview of the genome sequenceing process Presented by NIE , Lan CSE497 Feb.24, 2004
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Sequencing and Sequence Assembly
--overview of the genome sequenceing process
Presented by NIE , LanCSE497
Feb.24, 2004
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Introduction
Q: What is Sequence A: To sequence a DNA molecule is to obtain
the string of bases that it contains. Also know as read
Q: How to sequence A: Recall the Sanger Sequencing technology
mentioned in Chapter 1
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Introduction
Cut DNA at each base:A,C,G,T
Fragment’s migrate
distance is inversely
proportional to their
size
Sanger Sequencing
TCGCGATAGCTGTGCTA
Run gel and read off
sequence
4
Introduction
Limitation
The size of DNA fragments that can be read in this way is about 700 bps
Problem
Most genomes are enormous (e.g 108 base pair in case of human).So it is impossible to be sequenced directly! This is called Large-Scale Sequencing
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Introduction
Solution Break the DNA into small
fragments randomly Sequence the readable
fragment directly Assemble the fragment
together to reconstruct the original DNA
Scaffolder gaps
Solving a one-dimensional jigsaw puzzle with millions of pieces(without the box) !
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1. Break
2. Sequence
3. Assemble
4. Scaffolder
5. Conclusion
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Break
DNA can be cutten into pieces through mechanical means
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Issues in Break
How?
• Coverage
The whole fragments provide an 8X oversampling of
the genome
• Random
Libraries with pieces sizes of 2,4,6,10, 12 and 40 k bp were
produced
• Clone
Obtaining several copies of the original genome and fragments
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1. Break
2. Sequence
3. Assemble
4. Scaffolder
5. Conclusion
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Sequence
clone
Directed sequencing
(GEL)
GTCCAGCCT
Q: can we read the fragment from both end?
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1. Break
2. Sequence
3. Assemble
4. Scaffolder
5. Conclusion
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3. Assemble
A Simple Example
ACCGT CGTGC TTAC
Overlap: The suffix of a fragment is same as the prefix of another.
Assemble: align multiple fragments into single continuous sequence based on fragment overlap
--ACCGT
----CGTGC
TTAC
TTACCGTGC
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3. Assemble
fragments
fragments
assemble
target
original
contig1 contig2 gap
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A simple model
The simplest, naive approximation of DNA assemble corresponds to Shortest Superstring Problem(SCS): Given a set of string s1, ... , sn, find the shortest string s such that each si appears as a substring of s.
--ACCGT
----CGTGC
TTAC
TTACCGTGC
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(1) Overlap step
Create an overlap graph in which every node is a
fragment and edges indicate an overlap
(2) Layout step
Determine which overlaps will be used in
the final assembly, find an optimal spanning
forest on the overlap graph
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Overlap step
Finding overlap Compare each fragment with other fragments to find
whether there’s overlap on its end part and another’s beginning part.
We call ‘a overlap b’ when a’s suffix equal to b’s prefix
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Overlap step
Overlap graphDirected, weighted graph G(V,E,w)V: set of fragmentsE : set of directed edge indicates the overlap between two fragments. An edge <a,b,w> means an overlap between a and b with weight w. this equal to suffix(a,w)=prefix(b,w)
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Example
w
z
x
u
s y
W=AGTATTGGCAATC Z=AATCGATGU=ATGCAAACCTX=CCTTTTGGY=TTGGCAATCAS=AATCAGG
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3
9 4
5
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Layout step
Looking for shortest common superstring is the same as looking for path of maxium weight
Using greedy algorithm to select a edge with the best weight at every step.
The selected edge is checked by Rule. If this check is accepted, the edge is accepted, otherwise omit this edge
Rule: for either node on this edge, indegree and outdegree <=1; Acyclic
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At last the fragments merged together , from the point of graph, it is a forest of hamitonian paths(a path through the graph that contains each node at most once)., each path correspond to a contig
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Example
w
z
x
u
s y
W=AGTATTGGCAATC Z=AATCGATGU=ATGCAAACCTX=CCTTTTGGY=TTGGCAATCAS=AATCAGG
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3
9 4
5
W->Y->SAGTATTGGCAATC TTGGCAATCA AATCAGG
AGTATTGGCAATCAGG
Z->U->XAATCGATG
ATGCAAACCT
CCTTTTGG
AATCGATGCAAACCT TTTGG
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Geedy Algorithm is neither optimal nor complete, and will introduce gap
2
2
3
GCC
ATGC TGCAT
Can’t correctly model the assembly problem due to complication in the real problem instance
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Complication with Assemble
Sequencing errors. Most sequencers have around 1% error in the best case.
Unknown orientation. Could have sequenced either strand.
Bias in the reads. Not all regions of the sequence will be covered equally.
Repeats. There is much repetitive sequence, especially in human and higher plants
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Sequenceing Errors
Fragments contains3 kinds of errors: insert, deletion, substitution
Possibility :Substitutions ( 0.5-2% ), insert and deletion occur roughly 10 times less frequently
http://compbio.uchsc.edu/Hunter_lab/Hunter/bioi7711/lecture6.ppt
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x:ACCGT
Y:CGTGC
Z:TTAC
U:TACCGT
Problems with the simple model - Errors
A
G
3
x y
u z
3
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x y
u z
--ACCGT
----CGTGC
TTAC
-TACCGT
TTACCGTGC
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Problems with the simple model - Errors
Solution
Allow for bounded number of mismatches between overlapping fragments ----- Approximate overlaps
Criterion: minimum overlap length(40 bps), error rate(less than 6% mismatches )
How?
Using semi-global alignment to find the best match between the suffix of one sequence and the prefix of another.
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semi-global alignment
Score system: 1 for matches, -1 for mismatches, -2 for gaps
Initializing the first row and first column of zero, ignore gap in both extremities
Algorithm is same as global comparision
Search last column for higest score and obtain alignment by tracing back to start point ( overlap of x over y). overlap of y over x corresponds to the max in the last row
0000000000000……y0
0
0
x
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0 0 0 0 0 0
0 -1 1 1 -1 -2
0 -1 -1 0 2 0
0 1 -1 -2 1 1
0 -1 0 -2 -1 2
0 -1 -2 -1 -1 0
0 -1 0 -1 -2 -2
C
G
A
T
G
C
A C C G T
Overlap: y->x
CGATGC---
----ACCGT
Y:
X:
Overlap:x->yACCG-T—
--CGATGC
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x:ACCGT
Y:CGTGC
Z:TTAC
U:TACCGT
Problems with the simple model - Errors
A
G
3
x y
u z
3
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--ACCGT
----CGTGC
TTAC
-TACCGT
TTACCGTGC
--ACCG-T
----CGATGC
TT-C
-TAGCGT
TTACCGTGC
Criterion
1.Score>-3
2. Mismatch<2
2x y
u z
0
-2
0
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Problems with the simple model - Unkown orientation
Unknowns Orientation:
Fragments can be read from both of the DNA strands.Solution
Try all possible combination
CACGT
ACGT
ACTACG
GTACT
CACGT
ACGT
CGTAGT
AGTAC
CACGT
-ACGT
--CGTAGT
-----AGTAC
CACGTAGTACTGA
z
x
y
Z’
X’
Y’
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Problems with the simple model - Repeat
Repeats can be characterized by length, copy number & fidelity between copies– Human T-cell receptor: 5x of a 4kb gene w/ ~3%
variation– ALUs. ~300bp w/5-15% variation, clustering to be
50-60% of many human sequence regions– microsatellites, 3-6bp with thousands of repeats in
centromeric and telemeric regions, 1-2% variation.
gepard.bioinformatik.uni-saarland.de/html/BioinformatikIIIWS0304-Dateien/ V3-Assembly.ppt
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Problems with the simple model - Repeat2
Original OneDX1 B C X3X2A
X2X1
3X
AC B
D
Fragment
X3X2
X1
AC
BD
Assembler
Rearrangment
DX2 BC X1X3AConsensus
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Problems with the simple model - Repeat3
X1 B CX2AOriginal one
AX2X1
BC
Assembly
XA C BTarget one
gapContig 1 Contig2
Overcollapsing
! Shortest string is not always the
best!
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Problems with the simple model -Lack of coverage
Lack of coverage
Not all regions of the sequence will be covered equally
Target DNA
Uncovered area
Solution
Do more sampling to increase the coverage level
Using scaffolder technology
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1. Break
2. Sequence
3. Assemble
4. Scaffolder
5. Conclusion
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4. Scaffolder
Scaffold
Given a set of non-overlapping contigs, order and orient them to reconstruct the original DNA
How?
Is there any relationsip can be built between different contigs?
X B CA
X B’C’A
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4. Scaffolder -Mate Pairs
Mate pairs: The sequenced ends are facing towards each other The distance between the two fragments is known( insert size – fragment
size) The mate pairs is extremly valuable during the scaffold step.
Mate Pair
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4. Scaffolder -Method
• A scaffold retrieve the original mate pairs spanning in different contigs
• Using the link information of the pairs( Distance, Orientation) to orients contigs and estimates the gap size, this is calles “walk”
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4 Scaffolder -Example
Contig 1 Contig 2
gap
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4 Scaffolder
Graph Representation Nodes: contigs Directed edges: constraints on relative
placement of contigs – relative order and relative orientation
http://jbpc.mbl.edu/jbpc/GenomesMedia/10_14POP.PPT
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1. Break
2. Sequence
3. Assemble
4. Scaffolder
5. Conclusion
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5. Conclusion
The whole genome sequencing processBreak-> Sequence -> Assemble-> Scaffolder
A Simple Model Using overlap graph to construct the shortest common string However, it can’t corrctly model the assembly problem
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Conclusion-Repeat
• Repeat detection– pre-assembly: find fragments that belong to repeats
statistically (most existing assemblers) repeat database (RepeatMasker)
– during assembly: detect "tangles" indicative of repeats (Pevzner, Tang, Waterman 2001)
– post-assembly: find repetitive regions and potential mis-assemblies. (Reputer, RepeatMasker)
• Repeat resolution– find DNA fragments belonging to the repeat– determine correct tiling across the repeat
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