Lecture 1 Introduction to high throughput sequencing

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(No Transcript)
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Lecture 1Introduction to high throughput
sequencing

• Michael Brudno
• CSC 2431
• January 13, 2010

Adapted from presentations by Francis Ouelette,
OICR, Michael Stromberg, BC and Asim Siddiqui, ABI
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DNA sequencing

• How we obtain the sequence of nucleotides of a
  species

ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGAC
TACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG
ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT
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DNA Sequencing

• Goal
• Find the complete sequence of A, C, G, Ts 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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Generations of Sequences

• Sanger-style Classic
• 454 First Next-gen
• Illumina ABI SOLiD Next-gen
• Helicos 2.5 Gen
• PacBio Next-next-gen, 3rd gen

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Why are we sequencing?

• Before Next-generation
• DNA, RNA, (proteins), (populations), sampling,
  averages, consensus
• Problems sampling, averages, consensus.
• After Next-generation
• Genome sequence and structure
• Less cloning/PCR
• Single molecules (for some)

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Sanger (old-gen) Sequencing Now-Gen Sequencing
Whole Genome Human (early drafts), model organisms, bacteria, viruses and mitochondria (chloroplast), low coverage New human (!), individual genome, 1,000 normal, 25,000 cancer matched control pairs, rare-samples
RNA cDNA clones, ESTs, Full Length Insert cDNAs, other RNAs RNA-Seq Digitization of transcriptome, alternative splicing events, miRNA
Communities Environmental sampling, 16S RNA populations, ocean sampling, Human microbiome, deep environmental sequencing, Bar-Seq
Other Epigenome, rearrangements, ChIP-Seq
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Differences between the various platforms

• Nanotechnology used.
• Resolution of the image analysis.
• Chemistry and enzymology.
• Signal to noise detection in the software
• Software/images/file size/pipeline
• Cost

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Adapted from Richard Wilson, School of Medicine,
Washington University, Sequencing the Cancer
Genome http//tinyurl.com/5f3alk
Next Generation DNA Sequencing Technologies
Human Genome 6GB 6000 MB 6GB 6000 MB 6GB 6000 MB
Reqd Coverage 6 12 30
3730 454 Illumina
bp/read 600 400 2X75
reads/run 96 500,000 100,000.000
bp/run 57,600 0.5 GB 15 GB
runs reqd 625,000 144 12
runs/day 2 1 0.1
Machine days/human genome 312,500 (856 years) 144 120
Cost/run 48 6,800 9,300
Total cost 15,000,000 979,200 111,600
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Solexa-based Whole Genome Sequencing
Adapted from Richard Wilson, School of Medicine,
Washington University, Sequencing the Cancer
Genome http//tinyurl.com/5f3alk
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Illumina (Solexa)
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Illumina (Solexa)
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Illumina (Solexa)
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From Debbie Nickerson, Department of Genome
Sciences, University of Washington,
http//tinyurl.com/6zbzh4
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What is a base quality?
Base Quality Perror(obs. base)
3 50.12
5 31.62
10 10.00
15 3.16
20 1.00
25 0.32
30 0.10
35 0.03
40 0.01
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Next-gen sequencers
From John McPherson, OICR
100 Gb
AB/SOLiDv3, Illumina/GAII short-read sequencers
(10Gb in 50-100 bp reads, gt100M
reads, 4-8 days)
10 Gb
454 GS FLX pyrosequencer
1 Gb
(100-500 Mb in 100-400 bp reads, 0.5-1M reads,
5-10 hours)
bases per machine run
100 Mb
ABI capillary sequencer
10 Mb
(0.04-0.08 Mb in 450-800 bp reads, 96 reads,
1-3 hours)
1 Mb
10 bp
1,000 bp
100 bp
read length
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DNA sequencing vectors
DNA
Shake
DNA fragments
Known location (restriction site)
Vector Circular genome (bacterium, plasmid)


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Method to sequence longer regions
genomic segment
cut many times at random (Shotgun)
Get two reads from each segment
500 bp
500 bp
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Reconstructing the Sequence (Fragment Assembly)
reads
Cover region with 7-fold redundancy (7X)
Overlap reads and extend to reconstruct the
original genomic region
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Definition of Coverage
C

• Length of genomic segment L
• Number of reads n
• Length of each read l
• Definition Coverage C n l / L
• How much coverage is enough?
• Lander-Waterman model
• Assuming uniform distribution of reads, C10
  results in 1 gapped region /1,000,000 nucleotides

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Challenges with Fragment Assembly

• Sequencing errors
• 1-2 of bases are wrong
• Repeats
• Computation O( N2 ) where N reads

false overlap due to repeat
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History of DNA Sequencing
Adapted from Eric Green, NIH Adapted from
Messing Llaca, PNAS (1998)
1870
Miescher Discovers DNA
Avery Proposes DNA as Genetic Material
1940
Efficiency (bp/person/year)
Watson Crick Double Helix Structure of DNA
1953
Holley Sequences Yeast tRNAAla
1
15
1965
Wu Sequences ? Cohesive End DNA
150
1970
Sanger Dideoxy Chain Termination Gilbert
Chemical Degradation
1,500
1977
Messing M13 Cloning
15,000
1980
25,000
Hood et al. Partial Automation
50,000
1986

• Cycle Sequencing
• Improved Sequencing Enzymes
• Improved Fluorescent Detection Schemes

200,000
1990
50,000,000
2002

• Next Generation Sequencing
• Improved enzymes and chemistry
• New image processing

100,000,000,000
2009
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Which representative of the species?

• Which human?
• Answer one
• Answer two it doesnt matter
• Polymorphism rate number of letter changes
  between two different members of a species
• Humans 1/1,000 1/10,000
• Other organisms have much higher polymorphism
  rates

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Why humans are so similar

• A small population that interbred reduced the
  genetic variation
• Out of Africa 40,000 years ago

Out of Africa
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Migration of human variation

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

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Migration of human variation

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

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Migration of human variation

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

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Genetic Variations Why?
Phenotypic differences
Inherited diseases
Ancestral history
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Genetic Variations SNPs INDELs
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Structural Variations
Paul Medvedev review in prep July 2009
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SNP Discovery Goal
SNP
sequencing errors
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SNP Discovery Base Qualities
High quality
Low quality
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SNPs Bayesian Statistics
base quality
of individuals
allele call in read
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SNP Discovery
haploid
diploid
AACGTTAGCATA AACGTTAGCATA AACGTTAGCATA
AACGTTAGCATA AACGTTAGCATA AACGTTCGCATA AACGTTCGCAT
A
strain 1
individual 1
AACGTTCGCATA AACGTTCGCATA
strain 2
AACGTTCGCATA AACGTTCGCATA AACGTTCGCATA AACGTTCGCAT
A
AACGTTAGCATA AACGTTAGCATA AACGTTAGCATA
individual 2
strain 3
AACGTTAGCATA AACGTTAGCATA
individual 3
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Genotyping Consensus Generation
haploid
diploid
AACGTTAGCATA AACGTTAGCATA AACGTTAGCATA
AACGTTAGCATA AACGTTAGCATA AACGTTCGCATA AACGTTCGCAT
A
strain 1 A
individual 1 A/C
strain 2 C
AACGTTCGCATA AACGTTCGCATA
AACGTTCGCATA AACGTTCGCATA AACGTTCGCATA AACGTTCGCAT
A
individual 2 C/C
AACGTTAGCATA AACGTTAGCATA AACGTTAGCATA
strain 3 A
individual 3 A/A
AACGTTAGCATA AACGTTAGCATA
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Visualization Consed
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1000 Genomes Project
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1000G Goals

• Discover genetic variations
• 1 minor allele frequencies across genome
• 0.1 0.5 MAF across gene regions
• Variant alleles
• Estimate frequencies
• Identify haplotype background
• Characterize linkage disequilibrium

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1000G Pilot Projects

• Pilot 1
• Low coverage
• 180 samples
• 70 samples _at_ 4X
• 110 samples _at_ 2X
• 2.7 Tbp total
• 202 Gbp 454
• 1.8 Tbp Illumina
• 640 Gbp AB SOLiD

Pilot 2 Deep trios (CEU YRI) 6 samples 1.1 Tbp
total 87 Gbp 454 773 Gbp Illumina 270 Gbp AB SOLiD
Pilot 3 Exon capture 607 samples 2.2 Mbp of
targets 8800 targets 10 20x coverage
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Questions about the genome

• Obtaining a genome sequence is a one step towards
  understanding biological processes
• Questions that follow from the genome are
• What is transcribed?
• Where do proteins bind?
• What is methylated?
• In other words, how does it work?

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Central dogma
ZOOM IN
tRNA
transcription
DNA
rRNA
snRNA
translation
POLYPEPTIDE
mRNA
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Transcription

• The DNA is contained in the nucleus of the cell.
• A stretch of it unwinds there, and its message
  (or sequence) is copied onto a molecule of mRNA.
• The mRNA then exits from the cell nucleus.

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DNA
RNA
A T G C
T ? U
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More complexity

• The RNA message is sometimes edited.
• Exons are nucleotide segments whose codons will
  be expressed.
• Introns are intervening segments (genetic
  gibberish) that are snipped out.
• Exons are spliced together to form mRNA.

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Splicing

• frgjjthissentencehjfmkcontainsjunkelm
• thissentencecontainsjunk

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Key player RNA polymerase

• It is the enzyme that brings about transcription
  by going down the line, pairing mRNA nucleotides
  with their DNA counterparts.

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Promoters

• Promoters are sequences in the DNA just upstream
  of transcripts that define the sites of
  initiation.
• The role of the promoter is to attract RNA
  polymerase to the correct start site so
  transcription can be initiated.

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3
Promoter
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Promoters

• Promoters are sequences in the DNA just upstream
  of transcripts that define the sites of
  initiation.
• The role of the promoter is to attract RNA
  polymerase to the correct start site so
  transcription can be initiated.

5
3
Promoter
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Transcription key steps
DNA

• Initiation
• Elongation
• Termination

DNA

RNA
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Genes can be switched on/off

• In an adult multicellular organism, there is a
  wide variety of cell types seen in the adult. eg,
  muscle, nerve and blood cells.
• The different cell types contain the same DNA
  though.
• This differentiation arises because different
  cell types express different genes.
• Promoters are one type of gene regulators

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Transcription (recap)

• The DNA is contained in the nucleus of the cell.
• A stretch of it unwinds there, and its message
  (or sequence) is copied onto a molecule of mRNA.
• The mRNA then exits from the cell nucleus.
• Its destination is a molecular workbench in the
  cytoplasm, a structure called a ribosome.

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The Transcriptome

• The transcriptome is the entire set of RNA
  transcripts in the cell, tissue or organ.
• The transcriptome is cell type specific and time
  dependant i.e. It is a function of cell state
• The transcriptome can help us understand how
  cells differentiate and respond to changes in
  their environment.

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Transcriptome complexity

• Transcripts may be
• Modified
• Spliced
• Edited
• Degraded
• Transcriptome is substantially more complex than
  the genome and is time variant.

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ESTs

• ESTs were the first genome wide scan for
  transcriptional elements
• Different library types
• Proportional
• Normalized
• Subtractive
• Can be sequenced from the 5 or 3 end

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Hello Mr Chips

• Microarray chips introduced in 90s
• Parallel way to measure many genes
• Probes placed on slides
• RNA -gt cDNA, labelled with fluorescent dye and
  hybridized.
• Fluorescence measured
• Chips have been highly successful
• Simplified analysis
• Useful when there is no genome sequence
• Linear signal across 500 fold variation
• Standardization has aided use in medical
  diagnostics
• E.g. Mammaprint

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Microarray expression profiling by 2-color assay
(cDNA arrays)
Array PCR products 6250 yeast ORFs hybridized
cDNAs green control red experiment
Schena et al., 1995
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Chips pros and cons

• Advantages
• Do not require a genome sequence
• Highly characterised, with many s/w packages
  available
• One Affymetrix chip FDA approved
• Disadvantages
• Measurements limited to whats on the array
• Hard to distinguish isoforms when used for
  expression
• Cant detect balanced translocations or
  inversions when used for resequencing

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mRNA-seq

• Basic work flow
• Align reads (sometimes to transcriptome first and
  then the genome)
• Tally transcript counts
• Align tags to spliced transcripts
• Add to transcript counts

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Cloonan et al. 2008

• Used SOLiD to generate 10Gb of data from mouse
  embryonic stem cells and embryonic bodies
• Used a library of exon junctions to map across
  known splice events

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Distribution of tags
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Tag locations
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General issues

• Coverage across the transcript may not be random
• Some reads map to multiple locations
• Some reads dont map at all
• Reads mapping outside of known exons may
  represent
• New gene models
• New genes

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Size of the transcriptome

• Carter et al (2005)
• Using arrays estimated 520,000 to 850,000
  transcripts per cell.
• Use upper limit and estimate average transcript
  size of 2kb
• Transcriptome 2GB
• Transcriptome cost genome cost

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The Boundome

• DNA binding proteins control genome function
• Histones impact chromatin structure
• Activators and repressors impact gene expression
• The location of these proteins helps us
  understand how the genome works

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ChIP
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Chip-Seq

• Instead of probing against a chip, measure
  directly
• Basic work flow
• Align reads to the genome
• Identify clusters and peaks
• Determine bound sites

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Robertson et al. 2007

• Used Illumina technology to find STAT1 binding
  sites
• Comparisons with two ChIP-PCR data sets suggested
  that ChIP-seq sensitivity was between 70 and 92
  and specificity was at least 95.

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Tag statistics
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Typical Profile
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Mikkelsen et al., 2007

• Performed a comparison with ChIP-chip methods
  98 concordance

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Comparison with ChIP-seq
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The Methylome

• In methylated DNA, cytosines are methylated.
• This leads to silencing of genes in the region
  e.g. X inactivation
• It is yet another form of transcriptional control
  and together with histone modifications a key
  component of epigenetics

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Bi-sulphite sequencing

• Converts un-methylated cytosines to uracil (which
  becomes thymine when converted to cDNA)
• Experimental procedure is difficult
• Sequence alignment is tricky, but the basic
  concepts hold

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Taylor et al, 2007

• Targeted sequencing reduced alignment
  difficulties
• Used dynamic programming to identify alignments
  of sequences against an in silico bisulphate
  converted sequence of the target amplicon regions

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Metagenomics

• Craig Venters sequencing of the sea one of the
  earliest and most well known examples
• Used Sanger sequencing
• Many recent studies including
• Angly et al studied ocean virome
• Cox-Foster et al studied colony collapse
  disorder
• All use 454 for its longer read length and target
  amplification of 16S or 18S ribsomal subunits