1. The Changing Face of Sequencing
Strategies for de novo sequencing
of complex genomes

2. Quick Review:
BACs
Whole Genome Shotgun

3. First some history…. 2000: Arabidopsis 2005: Rice 2006: Poplar
BAC
2000: Arabidopsis
BAC & WGS
2005: Rice
WGS
2006: Poplar
WGS
2007: Grapevine
BAC
2008: Maize
WGS
2008: Papaya
WGS
2009: Sorghum

4. BAC-based vs WGS BAC-by-BAC WGS Pros Cons
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

5. 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

6. 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

7. does not One 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?

8. 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…..

9. 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

10. Scaffolds from individual BAC pools
BAC pooling strategy
Chr3. shortarm
Select FPC contigs
on the shortarm
FPC contigs
Select overlapping BACs
and bin them into 3Mb pools
3 Mb pools
Selected BACs
Pyrosequencing of BAC pools and
assembly of raw sequences
~20x 454 Titanium
Reads (~400bp each)
Contigs from
individual BAC
pools
454 FLX PE’s
(~250bp each)
Contigs are organized into scaffolds using
454 paired end sequences
Scaffolds from individual BAC pools
Use BAC ends
for very long scaffolds
Generate superscaffolds using
BAMBUS and BAC end sequences
Superscaffolds
spanning pool
boundaries
From Rounsley et al. (2009)

11. Results: Chr3S of Oryza barthii
6 x 3Mb BAC pools
1 Titanium Run
0.5 FLX Run
~$12k in reagents
Contig N50: 14.3 kb
Scaffold N50: kb
Scaffold N50: 3,165.1 kb
(after BAC ends)
Nt Accuracy: 2.2 errors per 10kb

12. 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

13. 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

14. Is WGS with NGS feasible yet?
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)

15. 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?

16. Building contigs from overlapping clones
5 overlapping BAC clones form small contig
Cut with R.E.
Overlapping BACs share common fragments

17. 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

18. 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

19. 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!

20. 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

21. 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

22. Summary No longer BACs vs WGS Different ways of using BACs
Linear pooling
2D pooling
BACs for map, WGS for sequence
WGS works on easy parts of genome
Simulation is valuable in evaluating strategies