some new sequencing technologies
DESCRIPTION
Some new sequencing technologies. Molecular Inversion Probes. Illumina Genotype Arrays. Single Molecule Array for Genotyping—Solexa. Nanopore Sequencing. http://www.mcb.harvard.edu/branton/index.htm. Pyrosequencing on a chip. Mostafa Ronaghi, Stanford Genome Technologies Center - PowerPoint PPT PresentationTRANSCRIPT
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Some new sequencing technologies
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Molecular Inversion Probes
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Illumina Genotype Arrays
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Nanopore Sequencing
http://www.mcb.harvard.edu/branton/index.htm
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Pyrosequencing on a chip
Mostafa Ronaghi, Stanford Genome Technologies Center
454 Life Sciences
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Polony Sequencing
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Technologies available today
• Illumina 550,000 SNP array: $300-500 in bulk
• 454 200 bp reads, 100 Mbp total sequence in 1 run, $8K 500bp reads in much higher throughput coming soon
• Solexa 1Gbp of sequence coming in paired 35 bp reads 1 day, approx $10K / run
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Short read sequencing protocol
• Random, high-coverage clone library (CovG = 7 – 10x)
• Low-coverage of clone by reads (CovR = 1 – 2x)
1234 1235
FRAGMENT
genome
clones
AMPLIFY & READ
12351234
reads
CovG
CovR
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Short read sequencing protocol
RANDOMLY SELECT 200,000 FRAGMENTS
CLONE
FRAGMENT AND SELECT 150KB SEGMENTS
FRAGMENT
A C G A
bead attachment primer
adapter
clone id tag
LIGATE ADAPTERS
166 clone batch
CLONE ON BEADS BY PCR EMULSION
ACGATGATCGATGATTAC...TGCTCAGACTTAGCTATT...CAATTTATATCAGAGACA...ACGAAATCGAGAGCAAGA...
clone id tag
SEQUENCE 250,000 READS ON PLATE
sequenceread
1200 plates
ASSEMBLY
target genome
“clones”
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Ordering clones into clone contigs
293
1001
1234
882
7
94
clone graph
NODE CONTRACTION
clone contig1234
2931001
947
882
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Contig assembly
CONSTRUCT READ SETS
Euler assembler
intersection read set
subtraction read set
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Contig assemblyCONTIG
ASSEMBLY 1: READ SETS
CONSTRUCT READ SETS
Euler assembler
CONSTRUCT CONTIG SETS
CONTIG ASSEMBLY 2: CONTIG SETS
Euler assembler
CONTIG ASSEMBLY 3:
CLONE CONTIGS
assembly
intersection read set
subtraction read set
contig set
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Assembly quality
Sequence Coverage Contig N50 (Kb)
Base quality (Q)
Misassemblies (#/Mb)
Small indels (#/Mb)
D. Melanogaster(118 Mb) 94.2% 160.2 38.4 2.5 1.6
Human chr21(34 Mb) 97.5% 79.0 35.6 1.9 2.3
Human chr11(131 Mb) 96.3% 57.4 34.4 2.8 1.9
Human chr1(223 Mb) 96.2% 63.0 34.4 3.0 2.0
Read length = 200 bp, Error rate = 1%, Net coverage = 20.0x
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Multiple Sequence Alignment
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Evolution at the DNA level
…ACGGTGCAGTTACCA…
…AC----CAGTCCACCA…
Mutation
SEQUENCE EDITS
REARRANGEMENTS
Deletion
InversionTranslocationDuplication
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Evolutionary Rates
OKOKOK
XX
Still OK?
next generation
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Genome Evolution – Macro Events
• Inversions• Deletions• Duplications
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Synteny maps
Comparison of human and mouse
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Synteny maps
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Orthology, Paralogy, Inparalogs, Outparalogs
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Synteny maps
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Synteny maps
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Building synteny maps
Recommended local aligners• BLASTZ
Most accurate, especially for genes Chains local alignments
• WU-BLAST Good tradeoff of efficiency/sensitivity Best command-line options
• BLAT Fast, less sensitive Good for
• comparing very similar sequences • finding rough homology map
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Index-based local alignment
Dictionary:All words of length k (~10)Alignment initiated between words of alignment score T
(typically T = k)
Alignment:Ungapped extensions until score
below statistical threshold
Output:All local alignments with score
> statistical threshold
……
……
query
DB
query
scan
Question: Using an idea from overlap detection, better way to find all local alignments between two genomes?
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Local Alignments
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After chaining
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Chaining local alignments
1. Find local alignments
2. Chain -O(NlogN) L.I.S.
3. Restricted DP
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Progressive Alignment
• When evolutionary tree is known:
Align closest first, in the order of the tree In each step, align two sequences x, y, or profiles px, py, to generate a new
alignment with associated profile presult
Weighted version: Tree edges have weights, proportional to the divergence in that edge New profile is a weighted average of two old profiles
x
w
y
z
Example
Profile: (A, C, G, T, -)px = (0.8, 0.2, 0, 0, 0)py = (0.6, 0, 0, 0, 0.4)
s(px, py) = 0.8*0.6*s(A, A) + 0.2*0.6*s(C, A) + 0.8*0.4*s(A, -) + 0.2*0.4*s(C, -)
Result: pxy = (0.7, 0.1, 0, 0, 0.2)
s(px, -) = 0.8*1.0*s(A, -) + 0.2*1.0*s(C, -)
Result: px- = (0.4, 0.1, 0, 0, 0.5)
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Threaded Blockset Aligner
Human–Cow
HMR – CDRestricted AreaProfile Alignment
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Reconstructing the Ancestral Mammalian Genome
Human: C
Baboon: C
Cat: C
Dog: G
C
C or G
G