DNA Sequencing. CS262 Lecture 9, Win07, Batzoglou DNA sequencing How we obtain the sequence of...

DNA Sequencing

CS262 Lecture 9, Win07, Batzoglou

DNA sequencing

How we obtain the sequence of nucleotides of a species

…ACGTGACTGAGGACCGTGCGACTGAGACTGACTGGGTCTAGCTAGACTACGTTTTATATATATATACGTCGTCGTACTGATGACTAGATTACAGACTGATTTAGATACCTGACTGATTTTAAAAAAATATT…

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!

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

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)

Migration of human variation

http://info.med.yale.edu/genetics/kkidd/point.html

Human variation in Y chromosome

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

DNA Sequencing

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

DNA Sequencing – vectors

DNA fragments

VectorCircular genome(bacterium, plasmid)

Knownlocation

(restrictionsite)

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

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

Electrophoresis diagrams

Challenging to read answer

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

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

Pyrosequencing / 454

24Image credits: 454 Life Sciences

Solexa / ABI SOLiD

25Image credits: Illumina, Applied Biosystems

Illumina / Affymetrix genotyping

26Image credits: Illumina, Affymetrix

Other technologies in development

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

Application Sanger 454 Solexa/ABISNP

Bacterial sequencing probably

Mammalian sequencing ? probably not

Mammalian resequencing

expensive expensive

Genotyping expensive expensive expensive

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

Reconstructing the Sequence (Fragment Assembly)

Cover region with ~7-fold redundancy (7X)

Overlap reads and extend to reconstruct the original genomic region

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

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

Sequencing and Fragment Assembly

AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT

3x109 nucleotides

50% of human DNA is composed of repeats

Error!Glued together two distant regions

What can we do about repeats?

Two main approaches:• Cluster the reads

• Link the reads

3x109 nucleotides

ARB, CRD

ARD, CRB ?

3x109 nucleotides

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

Hierarchical Sequencing

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

Methods of physical mapping

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

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

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

C2 …

……

1 0 1…………………...01 1 0 …………………..0

0 0 1 …………………..1

pi1pi2…………………….pim

j2 …

……

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

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

j2 …

……

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

j2 …

……

pi1pi2………………………………….pim

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

C2 …

……

1 0 1…………………...01 1 0 …………………..0

0 0 1 …………………..1

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

Online Clone-by-cloneThe Walking Method

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

Walking: An Example

Walking off a Single Seed

• Low redundant sequencing

• Many sequential steps

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 !

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

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

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

Fragment Assembly(in whole-genome shotgun sequencing)

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

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

consensus sequence derived from thesequene multiple alignment of reads

in a contig

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

• Find pairs of reads sharing a k-mer, k ~ 24• Extend to full alignment – throw away if not >98% similar

TAGATTACACAGATTAC

TAGATTACACAGATTAC|||||||||||||||||

TAGA| ||

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

Create local multiple alignments from the overlapping reads

TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGA

• 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

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

We want to merge reads up to potential repeat boundaries

repeat region

Unique Contig

Overcollapsed Contig

• Ignore non-maximal reads• Merge only maximal reads into contigs

repeat region

• 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

• Ignore “hanging” reads, when detecting repeat boundaries

sequencing error

repeat boundary???

Overlap graph after forming contigs

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

• Insert non-maximal reads whenever unambiguous

3. Link Contigs into Supercontigs

Too dense Overcollapsed

Inconsistent links Overcollapsed?

Normal density

Find all links between unique contigs

Connect contigs incrementally, if 2 links

supercontig(aka scaffold)

Fill gaps in supercontigs with paths of repeat contigs

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)

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

Quality of assemblies

Celera’s assemblies of human and mouse

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.

Quality of assemblies—rat

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

Genomes Sequenced

• http://www.genome.gov/10002154

DNA Sequencing. CS262 Lecture 9, Win07, Batzoglou DNA sequencing How we obtain the sequence of...

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