The document discusses the sequencing of large eukaryotic genomes, highlighting methods such as hierarchical shotgun sequencing and de Bruijn graphs. It details the specific case of the cod genome project, including the strategies employed, the assembly process, and challenges encountered. Additionally, it touches upon advancements in sequencing technology and bioinformatics for improved genome assembly.

1. How to sequence a large eukaryotic genomeand how we sequenced the cod genomeLex NederbragtNorwegian High-Throughput Sequencing Centre (NSC)andCentre for Ecological and Evolutionary Synthesis (CEES)

3. What is a genome assembly?A hierarchical data structurethat maps the sequence datato a putative reconstruction of the target Miller et al 2010, Genomics 95 (6): 315-327

4. Hierarchical structure

5. Sequence dataReadshttp://www.cbcb.umd.edu/research/assembly_primer.shtml

6. Reads!http://www.sciencephoto.com/media/210915/enlarge

7. ContigsBuilding contigs

8. ContigsBuilding contigsRepeat copy 1Repeat copy 2Contig orienation?Contig order?Collapsed repeat consensus http://www.cbcb.umd.edu/research/assembly_primer.shtml

9. Mate pairsOther read typeRepeat copy 1Repeat copy 2(much) longer fragmentsmate pair reads

10. ScaffoldsOrdered, oriented contigsmate pairscontigsgap size estimate

11. Hierarchical structure

12. AlgorithmsAll are graph-basedRead 2Read 1OverlapGraph-theory!

13. AlgorithmsHamiltonian patha path that contains all the nodeshttp://www.cbcb.umd.edu/research/assembly_primer.shtml

14. AlgorithmsOverlap calculation (alignment)computationally intensiveRead 2Read 1Overlap

15. AlgorithmsPath through the graphcontigRead 2Read 3Read 4Read 1OverlapOverlapOverlap

16. Greedy extensionOldesthttp://www.cbcb.umd.edu/research/assembly_primer.shtml

17. Overlap-Layout-ConsensusTypical for Sanger-type readsalso used by newbler from 454 Life SciencesStepsOverlap computationLayout: graph simplificationConsensus: sequence

18. Overlap-Layout-ConsensusOverlap phase:K-mer seeds initiate overlapACGCGATTCAGGTTACCACG

19. de Bruijn graphsDeveloped outside of DNA-related workBest solution for very short reads ≤100 ntGACCTACAGAC ACC CCT CTA TAC ACAReadde Bruijn graphK-mers (K=3)K-1 bases overlap

20. GraphsSchatz M C et al. Genome Res. 2010;20:1165-1173

21. GraphsSimplify the graphAdd scaffolding information

22. Sequence dataSequencing errorsadd complexity to graphcreate new k-mersCorrection of errorsk-mer frequencyKelley et al.Genome Biology 2010 11:R116

23. How to sequence a genomehuman 1990'scod 1 2009 - 2011cod 2  2011 - 2012

24. Human genomePublic effortBAC-by-BAC sequencinghierarchical shotgun sequencingGenomeBACsSelect BACs100-150 kb shotgun sequencinghttp://www.cbcb.umd.edu/research/assembly_primer.shtml

25. Human genomeCelera: shotgun sequencingentire genome shotgunuse of mate pairs

26. How to sequence a genome PreparationsBAC-by-BACAdd shotgunand mate pairs

27. The cod genome projectPreparations* From a different individual

28. Cod: strategy‘454 only’NO subcloningPure ‘shotgun’ approach454 specific paired end librariesSupplementaryBAC ends using Sanger sequencing

29. Cod: sequencing

30. Cod: assemblyInput for assembly84 million reads28 billion bases (Gb)34x coverageAssembly programNewbler from 454Celera from Venter Inst.Computing nodes24 cpus128 GB of memory

31. Cod: assembly611 Mb in 6 467 scaffoldsbut 35% gap basesshort contigsincomplete genes

32. Cod: gapsPolymorphiccontig 2HeterozygosityContig 4Contig 1Polymorphiccontig 3Short Tandem RepeatsACACACACACACACACACACACACACACACACACACACACACACACACACACACACACAACACACACACACACACACACACACACACACACACACACACACACACACACACACACACAACACACACACACACACACACACACACACACACACACACACACACACACACACACACACAACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA

33. Cod: annotationEnsembl'repair' genes based on stickleback sequence~22 000 geneshttp://pre.ensembl.org/Gadus_morhua/

35. Cod 2: 2011-2012Close the gapsincrease contig sizePseudochromosomesgenetic linkage mapscaffolds to 'chromosomes'anchoringordering and orienting

36. Cod 2: strategyNew dataIllumina readslonger 454 reads ~700 basesPacBio reads?Improved programsnewblerNew programsassemblygap closing

37. Many programs to choose from

38. Assembly competitionsAssemblathon 1simulated datasetsALLPATHS_LG – Broad Institute MIT (US)Soapdenovo – BGI (China)SGA – Sanger Institute (UK)

39. Assembly competitionsAssemblathon 2real datasetssnake – Illumina onlycichlid fish – Illumina onlyparrotIllumina454 FLX+PacBiohttp://assemblathon.org/

40. How to sequence a genomeIn 2011Cheap alternative: RAD-tag sequencing

41. How to sequence a genomeFoundation of Illumina data100x coverage Paired End reads (2x100bp)several Mate Pair libraries2kb, 3kb, 8k, 10kb, bigger?this is now very cheap!Fill gaps with long reads454 or PacBio

42. How to sequence a genomeAdd lots of bioinformatics...http://cores.montana.edu/index.php?page=bioinformatics-core-facility

43. Thank you!lex.nederbragt@bio.uio.nowww.sequencing.uio.nowww.sequencing.uio.no