Compartmentalized Shotgun Assembly CSA Two stated motivations Matcher

Compartmentalized Shotgun Assembly ? ? ? CSA Two stated motivations? ?

Matcher matched… …matched Celera reads with PFP BACTIGS, – 20. 76 million Celera reads matched (76%), – 0. 62 million had a mate pair that matched, • 2. 97 million Celera reads were unique and unscreened, – 1. 189 Gbp of unique DNA sequence, at 5. 11 X yields a predicted 240 Mbp of unique Celera sequence.

Combining Assembler assembles… “…Celera and PFP sequence for a transient assembly” …first, Celera reads, – are checked for over-collapsed regions, • sequences with Mate Pairs that match region are kept, • more mate pair matches = higher value assembly, …then Celera reads are combined with PFP reads, • “Greedy” program recognizes highest value assemblies first in order to build contigged sequence, …then “Stones” to fill the gaps.

Results… PFP vs. CSA • The Gen. Bank (PFP) data for the Phase 1 and 2 BACs yielded an average of 19. 8 bactigs per BAC, of average size 8099 bp, • Application of the Combining Assembler resulted in individual Celera/BAC assemblies being put together into an average of 1. 83 scaffolds (median of 1 scaffold) per BAC region consisting of an average of 8. 57 contigs of average size 18, 973 bp. pp. 1313, 1 st column, last paragraph

Compartmentalized Shotgun Assembly ?

Celera Unique Scaffolds WGA • The 5. 89 million Celera fragments not matching the Gen. Bank data were assembled with the whole-genome assembler. • The Celera assembly resulted in a set of scaffolds totaling 442 Mbp in span and consisting of 326 Mbp of sequence. More than 20% of the scaffolds were >5 kbp long, and these averaged 63% sequence and 27% gaps with a total of 302 Mbp of sequence.

Compartmentalized Shotgun Assembly ? ?

Tiler tiles… • Scaffolds into larger components using – Mate End Pairs, – BAC-end pairs, – STS, • Heuristic: a rule of thumb, simplification, or educated guess that reduces or limits the search for solutions in domains that are difficult and poorly understood. Unlike algorithms, heuristics do not guarantee optimal (or even feasible) solutions and are often used with no theoretical guarantee.

Compartmentalized Shotgun Assembly • 3, 845 Components • shredded, WGA *

93% • > 100 kbp Scaffolds; – > 49% gaps < 500 bp, – 92% sequence, 8% gaps, – 105, 264 gaps, 1, 935 scaffolds, – > 62% gaps < 1 kb, – 1. 3 Mbp scaffold size, 23, 242 bp – No gap larger than 100 kbp. contig size.

How do you compare assemblies?

WGA vs. CSA • This gives some measure of consistent coverage: – 1. 982 Gbp (95. 00%) of the WGA is covered by the CSA, – 2. 169 Gbp (87. 69%) of the CSA is covered by the WGA. • Only 31 scaffolds were ~unique to an assembly, • 295 kb (0. 012%) CSA inconsistent with WGA, • 2. 108 Mb (0. 11% WGA inconsistent with CSA, small regions Overall, CSA slightly better than WGA… Why? How does the CSA compare with the Clone-by-Clone approach?

Hierarchical Clone-by-Clone Map First: then sequence Whole Genome Assembly Sequence First: then map

Mapping Scaffolder GM 99 and fingerprint maps

? Tab. 4

Assembly and Validation Analysis …did it really work? • Completeness: % of euchromatic sequence in the assembly, – estimate the size and # of gaps (Table 3), WGA CSA 91 % Sequence 92. 2 % Sequence 92. 5 % Sequence 9 % Gaps 7. 8 % Gaps 12. 9 % Gaps 102, 068 Gaps 116, 442 Gaps PFP Small gaps (554 bp) = 145, 514 Gaps, Large gaps (35 kb) = 4076 Gaps.

Assembly and Validation Analysis …did it really work? • Completeness: % of euchromatic sequence in the assembly, – estimate the size and # of gaps (Table 3), – compare to “finished” sequences of 21, 22 • 3. 4 Mb gaps, 75% gaps are repeats, – match with STS data (e. PCR, BLAST), • 93. 4% tested found assembled, 5. 5% in “chaff” = 98. 9%, • Correctness: – Mate-Pair analysis.

Mate Pair Analysis Valid: correct orientation and correct distance + 3 SD 2. 7% were found to be invalid.

CSA vs. PFP What does this show?

Chromosome 21 Blue: Gaps Violations: Red : misoriented Yellow: distance PFP CSA Green: Same Order, Orientation Yellow: Same Orientation Red: Out of Order, Orientation

Chromosome 8 PFP CSA

PFP CSA

What’s the take home message?

PFP CSA Blue: breaks Red: gaps > 10 kb Fig. 7, key

Fig. 7

Gene Prediction and Annotation Why’s it So Hard to Find Genes? • Exons/Introns, • Alternative Splicing/Termination, • Alternate transcription start/stop sites, • Tandem Repeats, Psuedogenes, etc. • We don’t really understand all there is to know about gene and genome structure, • etc.

Gene Number Predictions? …before PFP, WGA or CSA Textbooks: ~100, 000 Upgraded to 142, 634? EST data “…counts [that] fall far short…” EST Data --> 35, 000 genes based on the density of Chromosome 22 28, 000 - 34, 000 Humans vs. pufferfish

Automated Gene Annotation OTTO Tell me how it works. How was it validated, including Table 7. …if necessary, use the Online Primer and other NCBI resources to broaden your understanding, – c. DNAs, ESTs, Ref. Seq, Protein Sequence Databases, BLAST, etc. are described in appropriate detail on the WEB.

Questions?

Repeat Resolver . . . most of the remaining gaps were due to repeats. “Rocks” Use “low Discriminator Value” contig sets to fill gaps, - find two or more mate pairs with unambiguous matches in the scaffold near the gap (2 kb, 10 kb or 50 kb), (1 in 107), “Stones” - find mate pair matches 2 kb, 10 kb, and 50 kb from gap, place the mate in the gap, check to see if it’s consistent with other “placed” sequences.