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 un- screened, –1.189 Gbp of unique DNA sequence, at 5.11X 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 GenBank (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, 1st column, last paragraph

Next Paper? Sorcerer II News? mycoplasma CR

Monday?

Compartmentalized Shotgun Assembly ?

Celera Unique Scaffolds WGA The 5.89 million Celera fragments not matching the GenBank 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

> 100 kbp Scaffolds; –92% sequence, 8% gaps, –105,264 gaps, 1,935 scaffolds, –1.3 Mbp scaffold size, 23,242 bp contig size. –> 49% gaps < 500 bp, –> 62% gaps < 1 kb, –No gap larger than 100 kbp. 93%

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, – 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, 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 Whole Genome Assembly Map First: then sequenceSequence First: then map

Mapping Scaffolder GM99 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), 92.2 % Sequence 7.8 % Gaps CSA 116,442 Gaps 91 % Sequence 9 % Gaps WGA 102,068 Gaps 92.5 % Sequence 12.9 % 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, Mb gaps, 75% gaps are repeats, –match with STS data (ePCR, 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?

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

Chromosome 8 PFP CSA

PFP CSA

What’s the take home message?

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

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 35,000 genes based on the density of Chromosome 22 28, ,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, –cDNAs, ESTs, RefSeq, 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, 10kb or 50 kb), (1 in 10 7 ), “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.

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, 10kb or 50 kb), (1 in 10 7 ), “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.