Transcript DNA Sequencing DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT… CS262 Lecture 9, Win07, Batzoglou.

DNA Sequencing
DNA sequencing
How we obtain the sequence of nucleotides of a species
…ACGTGACTGAGGACCGTG
CGACTGAGACTGACTGGGT
CTAGCTAGACTACGTTTTA
TATATATATACGTCGTCGT
ACTGATGACTAGATTACAG
ACTGATTTAGATACCTGAC
TGATTTTAAAAAAATATT…
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!
CS262 Lecture 9, Win07, Batzoglou
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
CS262 Lecture 9, Win07, Batzoglou
Why humans are so similar
Out of Africa
A small population that interbred
reduced the genetic variation
Out of Africa ~ 40,000 years ago
H = 4Nu/(1 + 4Nu)
CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
CS262 Lecture 9, Win07, Batzoglou
Migration of human variation
http://info.med.yale.edu/genetics/kkidd/point.html
CS262 Lecture 9, Win07, Batzoglou
Human variation in Y chromosome
CS262 Lecture 9, Win07, Batzoglou
CS262 Lecture 9, Win07, Batzoglou
CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing – Overview
•
Gel electrophoresis
1975
 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
2015
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
CS262 Lecture 9, Win07, Batzoglou
DNA Sequencing – vectors
DNA
Shake
DNA fragments
Vector
Circular genome
(bacterium, plasmid)
CS262 Lecture 9, Win07, Batzoglou
+
=
Known
location
(restriction
site)
Different types of vectors
VECTOR
Size of insert
Plasmid
2,000-10,000
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
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
CS262 Lecture 9, Win07, Batzoglou
Pyrosequencing / 454
Image credits: 454 Life Sciences
18
CS262 Lecture 9, Win07, Batzoglou
Solexa / ABI SOLiD
Image credits: Illumina, Applied Biosystems
19
CS262 Lecture 9, Win07, Batzoglou
Illumina / Affymetrix genotyping
Image credits: Illumina, Affymetrix
20
CS262 Lecture 9, Win07, Batzoglou
DNA sequencing’s Moore’s law
$10.00
Cost per finished
base:
$1.00
$0.10
$0.01
1990
CS262 Lecture 9, Win07, Batzoglou
2000
Growth of DNA Sequencing Capacity
Human Genome Project (1990 – 2003)
$3 billion
3 billion bases
2008
100,000 billion bases
2010
230,000 billion bases
2012
2,700,000 billion bases
CS262 Lecture 9, Win07, Batzoglou
Sequencing Applications
100 million species
Each individual
has different DNA
Within individual, some cells
have different DNA
(i.e. cancer)
CS262 Lecture 9, Win07, Batzoglou
Sequencing Applications
What genes are on/off,
when, and in which cells?
Where do molecules
bind to DNA?
CS262 Lecture 9, Win07, Batzoglou
Comparison
Technology
Read length (bp)
Pairing
bp / $
de novo
Sanger
1,000
long-range
1,000
yes
454
400
short-range
10,000
yes
Solexa/ABI
30-120
short-range
200,000
maybe
SNP chips
1
no
5,000
no
Application
Sanger
454
Solexa/ABI
Bacterial sequencing


probably
Mammalian sequencing

?
probably not
Mammalian
resequencing
expensive
expensive

Genotyping
expensive
expensive
expensive
SNP
chips

25
CS262 Lecture 9, Win07, Batzoglou
Method to sequence longer regions
genomic segment
cut many times at
random (Shotgun)
Get one or two reads from
each segment
~500 bp
CS262 Lecture 9, Win07, Batzoglou
~500 bp
Reconstructing the Sequence
(Fragment Assembly)
reads
Cover region with ~7-fold redundancy (7X)
Overlap reads and extend to reconstruct the original genomic
region
CS262 Lecture 9, Win07, Batzoglou
Definition of Coverage
C
Length of genomic segment:
Number of reads:
Length of each read:
L
n
l
Definition:
C=nl/L
Coverage
How much coverage is enough?
Lander-Waterman model:
Assuming uniform distribution of reads, C=10 results in 1
gapped region /1,000,000 nucleotides
CS262 Lecture 9, Win07, Batzoglou
Repeats
Bacterial genomes:
Mammals:
5%
50%
Repeat types:
•
Low-Complexity DNA (e.g. ATATATATACATA…)
•
Microsatellite repeats
•
Transposons
 SINE
(a1…ak)N where k ~ 3-6
(e.g. CAGCAGTAGCAGCACCAG)
(Short Interspersed Nuclear Elements)
e.g., ALU: ~300-long, 106 copies
 LINE
 LTR retroposons
(Long Interspersed Nuclear Elements)
~4000-long, 200,000 copies
(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
CS262 Lecture 9, Win07, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGA
CTACGACGAGA
CGATCGTGCGA
GCGACGGCGTA
GTGTGCTGTAC
TGTCGTGTGTG
TGTACTCTCCT
3x109 nucleotides
50% of human DNA is composed of repeats
Error!
Glued together two distant regions
CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:
• Cluster the reads
•
Link the reads
CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:
• Cluster the reads
•
Link the reads
CS262 Lecture 9, Win07, Batzoglou
What can we do about repeats?
Two main approaches:
• Cluster the reads
•
Link the reads
CS262 Lecture 9, Win07, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGA
CTACGACGAGA
CGATCGTGCGA
GCGACGGCGTA
GTGTGCTGTAC
TGTCGTGTGTG
TGTACTCTCCT
3x109 nucleotides
A
R
B
ARB, CRD
or
C
CS262 Lecture 9, Win07, Batzoglou
R
D
ARD, CRB ?
Sequencing and Fragment Assembly
AGTAGCACAGA
CTACGACGAGA
CGATCGTGCGA
GCGACGGCGTA
GTGTGCTGTAC
TGTCGTGTGTG
TGTACTCTCCT
3x109 nucleotides
CS262 Lecture 9, Win07, Batzoglou
Strategies for whole-genome sequencing
1.
Hierarchical – Clone-by-clone
i.
ii.
iii.
Break genome into many long pieces
Map each long piece onto the genome
Sequence each piece with shotgun
Example: Yeast, Worm, Human, Rat
2.
Online version of (1) – Walking
i.
ii.
iii.
Break genome into many long pieces
Start sequencing each piece with shotgun
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
CS262 Lecture 9, Win07, Batzoglou
Hierarchical Sequencing
CS262 Lecture 9, Win07, Batzoglou
Hierarchical Sequencing Strategy
a BAC clone
genome
1.
2.
3.
4.
5.
6.
Obtain a large collection of BAC clones
Map them onto the genome (Physical Mapping)
Select a minimum tiling path
Sequence each clone in the path with shotgun
Assemble
Put everything together
CS262 Lecture 9, Win07, Batzoglou
map
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
CS262 Lecture 9, Win07, Batzoglou
1.
Hybridization
p1
Short words, the probes, attach to complementary words
1.
2.
3.
4.
Construct many probes
Treat each BAC with all probes
Record which ones attach to it
Same words attaching to BACS X, Y  overlap
CS262 Lecture 9, Win07, Batzoglou
pn
2.
Digestion
Restriction enzymes cut DNA where specific words
appear
1. Cut each clone separately with an enzyme
2. Run fragments on a gel and measure length
3. Clones Ca, Cb have fragments of length { li, lj, lk } 
overlap
Double digestion:
Cut with enzyme A, enzyme B, then enzymes A + B
CS262 Lecture 9, Win07, Batzoglou
Some Terminology
insert a fragment that was incorporated in a
circular genome, and can be copied
(cloned)
vector the circular genome (host) that
incorporated the fragment
BAC
read
Bacterial Artificial Chromosome, a type
of insert–vector combination, typically
of length 100-200 kb
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
CS262 Lecture 9, Win07, Batzoglou
Whole Genome Shotgun
Sequencing
genome
cut many times at
random
plasmids (2 – 10 Kbp)
known dist
cosmids (40 Kbp)
~500 bp
CS262 Lecture 9, Win07, Batzoglou
forward-reverse paired
reads
~500 bp
Fragment Assembly
(in whole-genome shotgun sequencing)
CS262 Lecture 9, Win07, Batzoglou
Fragment Assembly
Given N reads…
Where N ~ 30
million…
We need to use a
linear-time
algorithm
CS262 Lecture 9, Win07, Batzoglou
Steps to Assemble a Genome
Some Terminology
1. Find
overlapping
reads
read
a 500-900
long word
that comes
out of sequencer
mate pair a pair of reads from two ends
2. Merge
some
pairs of reads into
of the
same“good”
insert fragment
longer contigs
contig
a contiguous sequence formed
by several overlapping reads
with
no gaps
3. Link
contigs
to form supercontigs
supercontig an ordered and oriented set
(scaffold)
of contigs, usually by mate
pairs
4. Derive consensus sequence
consensus sequence derived from the
sequene
multiple alignment of reads
in a contig
CS262 Lecture 9, Win07, Batzoglou
..ACGATTACAATAGGTT..
1. Find Overlapping Reads
aaactgcagtacggatct
aaactgcag
aactgcagt
…
gtacggatct
tacggatct
gggcccaaactgcagtac
gggcccaaa
ggcccaaac
…
actgcagta
ctgcagtac
gtacggatctactacaca
gtacggatc
tacggatct
…
ctactacac
tactacaca
CS262 Lecture 9, Win07, Batzoglou
(read, pos., word, orient.)
(word, read, orient., pos.)
aaactgcag
aactgcagt
actgcagta
…
gtacggatc
tacggatct
gggcccaaa
ggcccaaac
gcccaaact
…
actgcagta
ctgcagtac
gtacggatc
tacggatct
acggatcta
…
ctactacac
tactacaca
aaactgcag
aactgcagt
acggatcta
actgcagta
actgcagta
cccaaactg
cggatctac
ctactacac
ctgcagtac
ctgcagtac
gcccaaact
ggcccaaac
gggcccaaa
gtacggatc
gtacggatc
tacggatct
tacggatct
tactacaca
1. Find Overlapping Reads
• Find pairs of reads sharing a k-mer, k ~ 24
• Extend to full alignment – throw away if not >98% similar
TACA TAGATTACACAGATTAC T GA
|| ||||||||||||||||| | ||
TAGT TAGATTACACAGATTAC TAGA
• 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
CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
Create local multiple alignments from the
overlapping reads
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
CS262 Lecture 9, Win07, Batzoglou
1. Find Overlapping Reads
• Correct errors using multiple alignment
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAG-TTACACAGATTACTGA
insert A
replace T with C
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
correlated errors—
probably caused by repeats
 disentangle overlaps
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
In practice, error correction removes
up to 98% of the errors
CS262 Lecture 9, Win07, Batzoglou
TAG-TTACACAGATTATTGA
TAG-TTACACAGATTATTGA
2. Merge Reads into Contigs
• Overlap graph:
 Nodes: reads r1…..rn
 Edges: overlaps (ri, rj, shift, orientation, score)
Reads that come
from two regions of
the genome (blue
and red) that contain
the same repeat
Note:
of course, we don’t
know the “color” of
these nodes
CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
repeat region
Unique Contig
Overcollapsed Contig
We want to merge reads up to potential repeat boundaries
CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
repeat region
• Ignore non-maximal reads
• Merge only maximal reads into contigs
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)
CS262 Lecture 9, Win07, Batzoglou
r
r1
r2
r3
2. Merge Reads into Contigs
CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
repeat boundary???
a
sequencing error
b
…
b
a
• Ignore “hanging” reads, when detecting repeat boundaries
CS262 Lecture 9, Win07, Batzoglou
Overlap graph after forming contigs
CS262 Lecture 9, Win07, Batzoglou
Unitigs:
Gene Myers, 95
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
CS262 Lecture 9, Win07, Batzoglou
2. Merge Reads into Contigs
• Insert non-maximal reads whenever unambiguous
CS262 Lecture 9, Win07, Batzoglou
3. Link Contigs into Supercontigs
Normal density
Too dense
 Overcollapsed
Inconsistent links
 Overcollapsed?
CS262 Lecture 9, Win07, Batzoglou
3. Link Contigs into Supercontigs
Find all links between unique contigs
Connect contigs incrementally, if  2 links
supercontig
(aka scaffold)
CS262 Lecture 9, Win07, Batzoglou
3. Link Contigs into Supercontigs
Fill gaps in supercontigs with paths of repeat contigs
CS262 Lecture 9, Win07, Batzoglou
4. Derive Consensus Sequence
TAGATTACACAGATTACTGA TTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAAACTA
TAG TTACACAGATTATTGACTTCATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGGGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
Derive multiple alignment from pairwise read alignments
Derive each consensus base by weighted voting
(Alternative: take maximum-quality letter)
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
CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies
CS262 Lecture 9, Win07, Batzoglou
Celera’s assemblies of human and mouse
Quality of assemblies—mouse
CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies—mouse
Terminology: N50 contig length
If we sort contigs from largest to smallest, and start
Covering the genome in that order, N50 is the length
Of the contig that just covers the 50th percentile.
CS262 Lecture 9, Win07, Batzoglou
Quality of assemblies—rat
CS262 Lecture 9, Win07, Batzoglou
History of WGA
1997
• 1982: -virus, 48,502 bp
• 1995: h-influenzae,
Let’s sequence
the human
1 genome
Mbp with the
shotgun strategy
• 2000: fly, 100 Mbp
• 2001 – present
That
 human (3Gbp), mouse (2.5Gbp),
ratis*, chicken, dog, chimpanzee,
several fungal genomes impossible, and a
bad idea anyway
Gene Myers
CS262 Lecture 9, Win07, Batzoglou
Phil Green
Genomes Sequenced
• http://www.genome.gov/10002154
CS262 Lecture 9, Win07, Batzoglou