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DNA Sequencing
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CS262 Lecture 9, Win07, Batzoglou
DNA sequencing
How we obtain the sequence of nucleotides of a species
…ACGTGACTGAGGACCGTGCGACTGAGACTGACTGGGTCTAGCTAGACTACGTTTTATATATATATACGTCGTCGTACTGATGACTAGATTACAGACTGATTTAGATACCTGACTGATTTTAAAAAAATATT…
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CS262 Lecture 9, Win07, Batzoglou
Which representative of the species?
Which human?
Answer one:
Answer two: it doesn’t matter
Polymorphism rate: number of letter changes between two different members of a species
Humans: ~1/1,000
Other organisms have much higher polymorphism rates Population size!
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CS262 Lecture 9, Win07, Batzoglou
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CS262 Lecture 9, Win07, Batzoglou
Human population migrations
• Out of Africa, Replacement Single mother of all humans (Eve) ~150,000yr Single father of all humans (Adam) ~70,000yr Humans out of Africa ~40000 years ago
replaced others (e.g., Neandertals) Evidence: mtDNA
• Multiregional Evolution Fossil records show a continuous change of
morphological features Proponents of the theory doubt mtDNA and
other genetic evidence
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CS262 Lecture 9, Win07, Batzoglou
Why humans are so similar
A small population that interbred reduced the genetic variation
Out of Africa ~ 40,000 years ago
Out of Africa
H = 4Nu/(1 + 4Nu)
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CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
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CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
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CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
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CS262 Lecture 9, Win07, Batzoglou
Human variation in Y chromosome
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CS262 Lecture 9, Win07, Batzoglou
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CS262 Lecture 9, Win07, Batzoglou
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CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing – Overview
• Gel electrophoresis Predominant, old technology by F. Sanger
• Whole genome strategies Physical mapping Walking Shotgun sequencing
• Computational fragment assembly
• The future—new sequencing technologies Pyrosequencing, single molecule methods, … Assembly techniques
• Future variants of sequencing Resequencing of humans Microbial and environmental sequencing Cancer genome sequencing
1975
2015
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CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing
Goal:
Find the complete sequence of A, C, G, T’s in DNA
Challenge:
There is no machine that takes long DNA as an input, and gives the complete sequence as output
Can only sequence ~500 letters at a time
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CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing – vectors
+ =
DNA
Shake
DNA fragments
VectorCircular genome(bacterium, plasmid)
Knownlocation
(restrictionsite)
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CS262 Lecture 9, Win07, Batzoglou
Different types of vectors
VECTOR Size of insert
Plasmid2,000-10,000
Can control the size
Cosmid 40,000
BAC (Bacterial Artificial Chromosome)
70,000-300,000
YAC (Yeast Artificial Chromosome)
> 300,000
Not used much recently
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CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing – gel electrophoresis
1. Start at primer(restriction site)
2. Grow DNA chain
3. Include dideoxynucleoside (modified a, c, g, t)
4. Stops reaction at all possible points
5. Separate products with length, using gel electrophoresis
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CS262 Lecture 9, Win07, Batzoglou
Electrophoresis diagrams
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CS262 Lecture 9, Win07, Batzoglou
Challenging to read answer
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CS262 Lecture 9, Win07, Batzoglou
Challenging to read answer
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CS262 Lecture 9, Win07, Batzoglou
Challenging to read answer
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CS262 Lecture 9, Win07, Batzoglou
Reading an electropherogram
1. Filtering
2. Smoothening
3. Correction for length compressions
4. A method for calling the letters – PHRED
PHRED – PHil’s Read EDitor (by Phil Green)
Several better methods exist, but labs are reluctant to change
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CS262 Lecture 9, Win07, Batzoglou
Output of PHRED: a read
A read: 500-1000 nucleotides
A C G A A T C A G …A
16 18 21 23 25 15 28 30 32 …21
Quality scores: -10log10Prob(Error)
Reads can be obtained from leftmost, rightmost ends of the insert
Double-barreled sequencing: (1990)
Both leftmost & rightmost ends are sequenced, reads are paired
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CS262 Lecture 9, Win07, Batzoglou
Pyrosequencing / 454
24Image credits: 454 Life Sciences
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CS262 Lecture 9, Win07, Batzoglou
Solexa / ABI SOLiD
25Image credits: Illumina, Applied Biosystems
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CS262 Lecture 9, Win07, Batzoglou
Illumina / Affymetrix genotyping
26Image credits: Illumina, Affymetrix
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CS262 Lecture 9, Win07, Batzoglou
Other technologies in development
27
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CS262 Lecture 9, Win07, Batzoglou
Comparison
Technology Read length (bp) Pairing bp / $ de novo
Sanger 1,000 long-range 1,000 yes
454 250 short-range 10,000 yes
Solexa/ABI 30 short-range 100,000 maybe
SNP chips 1 no 5,000 no
28
Application Sanger 454 Solexa/ABISNP
chips
Bacterial sequencing probably
Mammalian sequencing ? probably not
Mammalian resequencing
expensive expensive
Genotyping expensive expensive expensive
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CS262 Lecture 9, Win07, Batzoglou
Method to sequence longer regions
cut many times at random (Shotgun)
genomic segment
Get one or two reads from each segment
~500 bp ~500 bp
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CS262 Lecture 9, Win07, Batzoglou
Reconstructing the Sequence (Fragment Assembly)
Cover region with ~7-fold redundancy (7X)
Overlap reads and extend to reconstruct the original genomic region
reads
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CS262 Lecture 9, Win07, Batzoglou
Definition of Coverage
Length of genomic segment: LNumber of reads: nLength of each read: l
Definition: Coverage C = n l / L
How much coverage is enough?
Lander-Waterman model:Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides
C
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CS262 Lecture 9, Win07, Batzoglou
Repeats
Bacterial genomes: 5%Mammals: 50%
Repeat types:
• Low-Complexity DNA (e.g. ATATATATACATA…)
• Microsatellite repeats (a1…ak)N where k ~ 3-6(e.g. CAGCAGTAGCAGCACCAG)
• Transposons SINE (Short Interspersed Nuclear Elements)
e.g., ALU: ~300-long, 106 copies LINE (Long Interspersed Nuclear Elements)
~4000-long, 200,000 copies LTR retroposons (Long Terminal Repeats (~700 bp) at each end)
cousins of HIV
• Gene Families genes duplicate & then diverge (paralogs)
• Recent duplications ~100,000-long, very similar copies
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CS262 Lecture 9, Win07, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
50% of human DNA is composed of repeats
Error!Glued together two distant regions
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CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
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CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
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CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
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CS262 Lecture 9, Win07, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
C R D
ARB, CRD
or
ARD, CRB ?
A R B
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CS262 Lecture 9, Win07, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
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CS262 Lecture 9, Win07, Batzoglou
Strategies for whole-genome sequencing
1. Hierarchical – Clone-by-clonei. Break genome into many long piecesii. Map each long piece onto the genomeiii. Sequence each piece with shotgun
Example: Yeast, Worm, Human, Rat
2. Online version of (1) – Walkingi. Break genome into many long piecesii. Start sequencing each piece with shotguniii. Construct map as you go
Example: Rice genome
3. Whole genome shotgun
One large shotgun pass on the whole genome
Example: Drosophila, Human (Celera), Neurospora, Mouse, Rat, Dog
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CS262 Lecture 9, Win07, Batzoglou
Hierarchical Sequencing
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CS262 Lecture 9, Win07, Batzoglou
Hierarchical Sequencing Strategy
1. Obtain a large collection of BAC clones2. Map them onto the genome (Physical Mapping)3. Select a minimum tiling path4. Sequence each clone in the path with shotgun5. Assemble6. Put everything together
a BAC clone
mapgenome
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CS262 Lecture 9, Win07, Batzoglou
Methods of physical mapping
Goal:
Make a map of the locations of each clone relative to one another
Use the map to select a minimal set of clones to sequence
Methods:
• Hybridization
• Digestion
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CS262 Lecture 9, Win07, Batzoglou
1. Hybridization
Short words, the probes, attach to complementary words
1. Construct many probes
2. Treat each BAC with all probes
3. Record which ones attach to it
4. Same words attaching to BACS X, Y overlap
p1 pn
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CS262 Lecture 9, Win07, Batzoglou
Hybridization – Computational Challenge
Matrix:m probes n clones
(i, j): 1, if pi hybridizes to Cj
0, otherwise
Definition: Consecutive ones matrix1s are consecutive in each row & col
Computational problem:Reorder the probes so that matrix is in consecutive-ones form
Can be solved in O(m3) time (m > n)
p1 p2 …………………….pm
C1
C2 …
……
……
….C
n
1 0 1…………………...01 1 0 …………………..0
0 0 1 …………………..1
pi1pi2…………………….pim
Cj1C
j2 …
……
……
….C
jn
1 1 1 0 0 0……………..00 1 1 1 1 1……………..00 0 1 1 1 0……………..0
0 0 0 0 0 0………1 1 1 00 0 0 0 0 0………0 1 1 1
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CS262 Lecture 9, Win07, Batzoglou
Hybridization – Computational Challenge
If we put the matrix in consecutive-ones form,
then we can deduce the order of the clones
& which pairs of clones overlap
pi1pi2…………………….pim
Cj1C
j2 …
……
……
….C
jn
1 1 1 0 0 0……………..00 1 1 1 1 1……………..00 0 1 1 1 0……………..0
0 0 0 0 0 0………1 1 1 00 0 0 0 0 0………0 1 1 1 C
j1C
j2 …
……
……
….C
jn
pi1pi2………………………………….pim
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CS262 Lecture 9, Win07, Batzoglou
Hybridization – Computational Challenge
Additional challenge:
A probe (short word) can hybridize in many places in the genome
Computational Problem:
Find the order of probes that implies the minimal probe repetition
Equivalent: find the shortest string of probes such that each clone appears as a substring
APX-hard
Solutions:Greedy, probabilistic, lots of manual curation
p1 p2 …………………….pm
C1
C2 …
……
……
….C
n
1 0 1…………………...01 1 0 …………………..0
0 0 1 …………………..1
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CS262 Lecture 9, Win07, Batzoglou
2. Digestion
Restriction enzymes cut DNA where specific words appear
1. Cut each clone separately with an enzyme2. Run fragments on a gel and measure length3. Clones Ca, Cb have fragments of length { li, lj, lk }
overlap
Double digestion:Cut with enzyme A, enzyme B, then enzymes A + B
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CS262 Lecture 9, Win07, Batzoglou
Online Clone-by-cloneThe Walking Method
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CS262 Lecture 9, Win07, Batzoglou
The Walking Method
1. Build a very redundant library of BACs with sequenced clone-ends (cheap to build)
2. Sequence some “seed” clones
3. “Walk” from seeds using clone-ends to pick library clones that extend left & right
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CS262 Lecture 9, Win07, Batzoglou
Walking: An Example
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CS262 Lecture 9, Win07, Batzoglou
Walking off a Single Seed
• Low redundant sequencing
• Many sequential steps
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CS262 Lecture 9, Win07, Batzoglou
Walking off a single clone is impractical
Cycle time to process one clone: 1-2 months
1. Grow clone2. Prepare & Shear DNA3. Prepare shotgun library & perform shotgun4. Assemble in a computer5. Close remaining gaps
A mammalian genome would need 15,000 walking steps !
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CS262 Lecture 9, Win07, Batzoglou
Walking off several seeds in parallel
• Few sequential steps
• Additional redundant sequencing
In general, can sequence a genome in ~5 walking steps, with <20% redundant sequencing
Efficient Inefficient
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CS262 Lecture 9, Win07, Batzoglou
Some Terminologyinsert a fragment that was incorporated in a circular genome, and can be copied (cloned)
vector the circular genome (host) that incorporated the fragment
BAC Bacterial Artificial Chromosome, a type of insert–vector combination, typically of length 100-200 kb
read a 500-900 long word that comes out of a sequencing machine
coverage the average number of reads (or inserts) that cover a position in the target DNA piece
shotgun the process of obtaining many reads sequencing from random locations in DNA, to
detect overlaps and assemble
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CS262 Lecture 9, Win07, Batzoglou
Whole Genome Shotgun Sequencing
cut many times at random
genome
forward-reverse paired reads
plasmids (2 – 10 Kbp)
cosmids (40 Kbp) known dist
~500 bp~500 bp
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CS262 Lecture 9, Win07, Batzoglou
Fragment Assembly(in whole-genome shotgun sequencing)
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CS262 Lecture 9, Win07, Batzoglou
Fragment Assembly
Given N reads…Given N reads…Where N ~ 30 Where N ~ 30
million…million…
We need to use a We need to use a linear-time linear-time algorithmalgorithm
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CS262 Lecture 9, Win07, Batzoglou
Steps to Assemble a Genome
1. Find overlapping reads
4. Derive consensus sequence ..ACGATTACAATAGGTT..
2. Merge some “good” pairs of reads into longer contigs
3. Link contigs to form supercontigs
Some Terminology
read a 500-900 long word that comes out of sequencer
mate pair a pair of reads from two endsof the same insert fragment
contig a contiguous sequence formed by several overlapping readswith no gaps
supercontig an ordered and oriented set(scaffold) of contigs, usually by mate
pairs
consensus sequence derived from thesequene multiple alignment of reads
in a contig
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CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
aaactgcagtacggatctaaactgcag aactgcagt… gtacggatct tacggatctgggcccaaactgcagtacgggcccaaa ggcccaaac… actgcagta ctgcagtacgtacggatctactacacagtacggatc tacggatct… ctactacac tactacaca
(read, pos., word, orient.)
aaactgcagaactgcagtactgcagta… gtacggatctacggatctgggcccaaaggcccaaacgcccaaact…actgcagtactgcagtacgtacggatctacggatctacggatcta…ctactacactactacaca
(word, read, orient., pos.)
aaactgcagaactgcagtacggatcta actgcagta actgcagtacccaaactgcggatctacctactacacctgcagtacctgcagtacgcccaaactggcccaaacgggcccaaagtacggatcgtacggatctacggatcttacggatcttactacaca
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CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
• Find pairs of reads sharing a k-mer, k ~ 24• Extend to full alignment – throw away if not >98% similar
TAGATTACACAGATTAC
TAGATTACACAGATTAC|||||||||||||||||
T GA
TAGA| ||
TACA
TAGT||
• Caveat: repeats A k-mer that occurs N times, causes O(N2) read/read comparisons ALU k-mers could cause up to 1,000,0002 comparisons
• Solution: Discard all k-mers that occur “too often”
• Set cutoff to balance sensitivity/speed tradeoff, according to genome at hand and computing resources available
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CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
Create local multiple alignments from the overlapping reads
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGA
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CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
• Correct errors using multiple alignment
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTATTGATAGATTACACAGATTACTGATAG-TTACACAGATTACTGA
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG-TTACACAGATTATTGATAGATTACACAGATTACTGATAG-TTACACAGATTATTGA
insert A
replace T with Ccorrelated errors—probably caused by repeats disentangle overlaps
TAGATTACACAGATTACTGATAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
In practice, error correction removes up to 98% of the errors
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
• Overlap graph: Nodes: reads r1…..rn
Edges: overlaps (ri, rj, shift, orientation, score)
Note:of course, we don’tknow the “color” ofthese nodes
Reads that comefrom two regions ofthe genome (blueand red) that containthe same repeat
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
We want to merge reads up to potential repeat boundaries
repeat region
Unique Contig
Overcollapsed Contig
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
• Ignore non-maximal reads• Merge only maximal reads into contigs
repeat region
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
• Remove transitively inferable overlaps If read r overlaps to the right reads r1, r2,
and r1 overlaps r2, then (r, r2) can be inferred by (r, r1) and (r1, r2)
r r1 r2 r3
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
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CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
• Ignore “hanging” reads, when detecting repeat boundaries
sequencing error
repeat boundary???
ba
a
b
…
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CS262 Lecture 9, Win07, Batzoglou
Overlap graph after forming contigs
Unitigs:Gene Myers, 95
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CS262 Lecture 9, Win07, Batzoglou
Repeats, errors, and contig lengths
• Repeats shorter than read length are easily resolved Read that spans across a repeat disambiguates order of flanking regions
• Repeats with more base pair diffs than sequencing error rate are OK We throw overlaps between two reads in different copies of the repeat
• To make the genome appear less repetitive, try to:
Increase read length Decrease sequencing error rate
Role of error correction:Discards up to 98% of single-letter sequencing errors
decreases error rate decreases effective repeat content increases contig length
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CS262 Lecture 9, Win07, Batzoglou
• Insert non-maximal reads whenever unambiguous
2. Merge Reads into Contigs
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CS262 Lecture 9, Win07, Batzoglou
3. Link Contigs into Supercontigs
Too dense Overcollapsed
Inconsistent links Overcollapsed?
Normal density
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CS262 Lecture 9, Win07, Batzoglou
Find all links between unique contigs
3. Link Contigs into Supercontigs
Connect contigs incrementally, if 2 links
supercontig(aka scaffold)
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CS262 Lecture 9, Win07, Batzoglou
Fill gaps in supercontigs with paths of repeat contigs
3. Link Contigs into Supercontigs
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CS262 Lecture 9, Win07, Batzoglou
4. Derive Consensus Sequence
Derive multiple alignment from pairwise read alignments
TAGATTACACAGATTACTGA TTGATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGCGTAAACTATAG TTACACAGATTATTGACTTCATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGCGTAA CTATAGATTACACAGATTACTGACTTGATGGGGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
Derive each consensus base by weighted voting
(Alternative: take maximum-quality letter)
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CS262 Lecture 9, Win07, Batzoglou
Some Assemblers
• PHRAP• Early assembler, widely used, good model of read errors
• Overlap O(n2) layout (no mate pairs) consensus
• Celera• First assembler to handle large genomes (fly, human, mouse)
• Overlap layout consensus
• Arachne• Public assembler (mouse, several fungi)
• Overlap layout consensus
• Phusion• Overlap clustering PHRAP assemblage consensus
• Euler• Indexing Euler graph layout by picking paths consensus
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CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies
Celera’s assemblies of human and mouse
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CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies—mouse
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CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies—mouse
Terminology: N50 contig lengthN50 contig lengthIf we sort contigs from largest to smallest, and startCovering the genome in that order, N50 is the lengthOf the contig that just covers the 50th percentile.
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CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies—rat
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CS262 Lecture 9, Win07, Batzoglou
History of WGA
• 1982: -virus, 48,502 bp
• 1995: h-influenzae, 1 Mbp
• 2000: fly, 100 Mbp
• 2001 – present human (3Gbp), mouse (2.5Gbp), rat*, chicken, dog, chimpanzee,
several fungal genomes
Gene Myers
Let’s sequence the human
genome with the shotgun
strategy
That is impossible, and
a bad idea anyway
Phil Green
1997
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CS262 Lecture 9, Win07, Batzoglou
Genomes Sequenced
• http://www.genome.gov/10002154