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First some history…. 2000: ArabidopsisBAC
BAC & WGS
BAC
WGS
WGS
WGS
WGS
2005: Rice
2006: Poplar
2007: Grapevine
2008: Maize
2008: Papaya
2009: Sorghum
BAC-based vs WGS
BAC-by-BAC WGS
Pros• Simpler more accurate assembly• Localized sequence• Easily distributed• Can be targeted to regions
• Physical map not needed, but helps• Logistically simple• Low library costs• Rapid
Cons• Requires physical map• Labor intensive• Expensive (more libraries)• Slower
• Complex assembly• Harder to localize sequence• Requires centralized assembly• Whole genome or nothing
What made WGS possible?
Long, high quality Sanger reads (700-800bp) Paired-end libraries Range of insert sizes
3kb 8-10kb 40kb fosmids
Assemblers tailored to these datatypes. Still not guaranteed…
public maize project went BAC by BAC
NGS changes all the rules
Quantity not quality is now the focus New platforms generate huge quantities of data Read length & PE’s initially limited de novo apps
Rapid cycle of improvements No time for standard approaches to spread beyond
genome centers before next cycle begins. Third party software sometimes slow to catch up
Cost model has changed Library construction used to be minor component of cost Unit used to be 96 or 384 reads…..
Choice is now more complex than BAC vs WGS
does notOne size^fits all
Every project has individual needs Monolithic reference genome is rarely needed now How bad are the repeat structures? Is it important to get them right? How important is it to anchor all the sequence to a
genome location? What other genome data can be leveraged?
BACs and NGS – the problem
Pre-NGS: To sequence a BAC:
Make 1 sequencing library ~$50-100 Sequence two 384-well plates of clones ~$750 ~6x coverage
With NGS: To sequence a BAC with 454:
Make 1 sequencing library ~$300 Sequence 1/8 plate of 454: ~$1,000 ~600x coverage
Too expensive, and too much coverage…..
New BAC-based approaches
One library per BAC is cost-prohibitive
Map-based BAC pooling Retain some of the assembly benefits of BACs Reduced library costs over BAC-by-BAC If contiguous, retains the genome localization benefits
BAC pooling strategyChr3. shortarm
FPC contigs
Selected BACs
Contigs from individual BAC
pools
Scaffolds from individual BAC
pools
Superscaffolds spanning poolboundaries
Select FPC contigs on the shortarm
Select overlapping BACs and bin them into 3Mb pools
Pyrosequencing of BAC pools and assembly of raw sequences
Contigs are organized into scaffolds using 454 paired end sequences
Generate superscaffolds using BAMBUS and BAC end sequences
3 Mb pools
~20x 454 TitaniumReads (~400bp each)
454 FLX PE’s (~250bp each)
Use BAC ends for very long scaffolds
From Rounsley et al. (2009)
Results: Chr3S of Oryza barthii
6 x 3Mb BAC pools1 Titanium Run0.5 FLX Run
~$12k in reagents
Contig N50: 14.3 kbScaffold N50: 370.9 kbScaffold N50: 3,165.1 kb(after BAC ends)
Nt Accuracy: 2.2 errors per 10kb
2D pooling: An alternative to contiguous BAC pools
• Place ordered clones in plates• 1 Library from each row• 1 Library from each column• Identify reads from each individual clone
by sequence overlap.• Then assemble each clone
Assembly unit reduced to ~ single BAC Library cost drops with size of grid
10x10: 100 clones, 20 libraries 50x50: 2500 clones, 100 libraries
3D grid lowers cost even further 10x10x10: 1000 clones, 30 libraries 20x20x20: 4000 clones, 60 libraries
Repeats may misbehave but can choose to ignore them
The ideal….
One library per BAC clone Barcoded Sequence all clones from BAC library in one
combined, barcoded pool
BUT: currently not cost-effective. Individual DNA preps for thousands of BAC
clones is costly
Is WGS with NGS feasible yet?
With 454: 400bp reads, + 4kb and 20kb insert PE protocols Success may be Species & Goal dependent:
Arabidopsis small & low repeat content 21kb contig N50; 2.6Mb scaffold N50 Roche & Ecker
Cassava 800Mb, lots of repeats 5.3kb contig N50; 180kb scaffold N50 Roche & JGI Missing half of the genome (repetitive half)
WGS with Solexa/Illumina Improved read-lengths, PE protocols Improved third party assemblers
e.g. SOAPdenovo, Velvet
Cucumber genome - BGI 300Mb genome 50x coverage with 50bp PE 5kb contigN50, 60kb scaffoldN50 Much better when mixed with 4x Sanger Missing half of genome (repeats)
Panda Genome - BGI 3Gb genome 50x coverage with 75bp PE 300kb contigN50 (?)
Big question: What is misassembly rate?
Building contigs from overlapping clones
Cut with R.E.
Overlapping BACs share common fragments
5 overlapping BAC clones form small contig
Building contigs from overlapping clones
• Measure lengths
Overlapping BACs will share fragments of same size
• Make sequencing lib• Sequence from each cut site
Overlapping BACs will share sequence tags next to each cut site
A BAC-WGS hybrid? whole genome profiling by Keygene
A: Solexa-based BAC map Construct BAC library; array into 2D pools Cut with restriction enzyme, and make 1 library per pool. Generate sequence from libraries Deconvolute pools to identify the Solexa reads from each BAC. Build a map from overlaps Map has short sequence tag every 1-2kb in genome
B: WGS sequencing with Solexa Assemble short contigs (high stringency) Use above map to locate each contig in genome. Map can identify misassemblies
C: Result: High quality map-based genome at fraction of cost
Simulation of Tag-based Map building
Rice: 372Mb, 12 chromosomes Simulate a 10x BAC library
28,600 clones Cut the sequence for each clone with HindIII Simulate a short read sequence from each site
2.2 million sequence tags Build a map from these – overlapping clones share tags
33 contigs built (<3 contigs per chromosome) Only 1 misassembly!
So you want to sequence a genome?
Lots of choices to make: BACs, WGS Which NGS technology? Single end, paired end? What size paired ends? What depth of coverage from each?
How do you pick? Do lots of testing of strategies - $$$$$ Guess – Free Copy what someone else did - Free Educated Guess based on Simulation
How to decide on a strategy? Simulating Genome Sequencing
“Plantagora”Plant Genome Assembly Simulation Platform
Use existing genomes to simulate sequencing reads Combine reads in many combinations Assemble Score the results with meaningful metrics Report results on web site