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CS273a Lecture 4, Autumn 08, Batzoglou
DNA Sequencing
CS273a Lecture 4, Autumn 08, Batzoglou
DNA sequencing
How we obtain the sequence of nucleotides of a species
…ACGTGACTGAGGACCGTGCGACTGAGACTGACTGGGTCTAGCTAGACTACGTTTTATATATATATACGTCGTCGTACTGATGACTAGATTACAGACTGATTTAGATACCTGACTGATTTTAAAAAAATATT…
CS273a Lecture 4, Autumn 08, 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!
CS273a Lecture 4, Autumn 08, 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
Heterozygosity: H
H = 4Nu/(1 + 4Nu)
u ~ 10-8, N ~ 104
H ~ 410-4
N
CS273a Lecture 4, Autumn 08, Batzoglou
Human population migrations
• Out of Africa, Replacement “Grandma” of all humans (Eve) ~150,000yr
• Ancestor of all mtDNA
“Grandpa” of all humans (Adam) ~100,000yr• Ancestor of all Y-chromosomes
• Multiregional Evolution Fossil records show a continuous change of
morphological features Proponents of the theory doubt mtDNA and
other genetic evidence
• New fossil records bury “multirigionalists” Nice article in Economist on thathttp://www.economist.com/science/displaystory.cfm?story_id=9507453
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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 ~900 letters at a time
CS273a Lecture 4, Autumn 08, Batzoglou
DNA Sequencing – vectors
+ =
DNA
Shake
DNA fragments
VectorCircular genome(bacterium, plasmid)
Knownlocation
(restrictionsite)
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Method to sequence longer regions
cut many times at random (Shotgun)
genomic segment
Get one or two reads from each segment
~900 bp ~900 bp
CS273a Lecture 4, Autumn 08, Batzoglou
Reconstructing the Sequence (Fragment Assembly)
Cover region with high redundancy
Overlap & extend reads to reconstruct the original genomic region
reads
CS273a Lecture 4, Autumn 08, Batzoglou
Definition of Coverage
Length of genomic segment: GNumber of reads: NLength of each read: L
Definition: Coverage C = N L / G
How much coverage is enough?
Lander-Waterman model: Prob[ not covered bp ] = e-C
Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides
C
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
50% of human DNA is composed of repeats
Error!Glued together two distant regions
CS273a Lecture 4, Autumn 08, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
CS273a Lecture 4, Autumn 08, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
CS273a Lecture 4, Autumn 08, Batzoglou
What can we do about repeats?
Two main approaches:• Cluster the reads
• Link the reads
CS273a Lecture 4, Autumn 08, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
C R D
ARB, CRD
or
ARD, CRB ?
A R B
CS273a Lecture 4, Autumn 08, Batzoglou
Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Hierarchical Sequencing
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Online Clone-by-cloneThe Walking Method
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Walking: An Example
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
Whole Genome Shotgun Sequencing
cut many times at random
genome
forward-reverse paired reads
plasmids (2 – 10 Kbp)
cosmids (40 Kbp) known dist
~800 bp~800 bp
CS273a Lecture 4, Autumn 08, Batzoglou
Fragment Assembly(in whole-genome shotgun sequencing)
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, Batzoglou
1. Find Overlapping Reads
Create local multiple alignments from the overlapping reads
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGA
CS273a Lecture 4, Autumn 08, 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
CS273a Lecture 4, Autumn 08, 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