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How to sequence a large eukaryotic genome
- 1. How to sequence a large eukaryotic genomeand how we sequenced the cod genome Lex Nederbragt Norwegian High-Throughput Sequencing Centre (NSC) and Centre for Ecological and Evolutionary Synthesis (CEES)
- 3. What is a genome assembly? A hierarchical data structure that maps the sequence data to a putative reconstruction of the target Miller et al 2010, Genomics 95 (6): 315-327
- 5. Sequence data Reads http://www.cbcb.umd.edu/research/assembly_primer.shtml
- 8. Contigs Building contigs Repeat copy 1 Repeat copy 2 Contig orienation? Contig order? Collapsed repeat consensus http://www.cbcb.umd.edu/research/assembly_primer.shtml
- 9. Mate pairs Other read type Repeat copy 1 Repeat copy 2 (much) longer fragments mate pair reads
- 10. Scaffolds Ordered, oriented contigs mate pairs contigs gap size estimate
- 12. Algorithms All are graph-based Read 2 Read 1 Overlap Graph-theory!
- 13. Algorithms Hamiltonian path a path that contains all the nodes http://www.cbcb.umd.edu/research/assembly_primer.shtml
- 14. Algorithms Overlap calculation (alignment) computationally intensive Read 2 Read 1 Overlap
- 15. Algorithms Path through the graph contig Read 2 Read 3 Read 4 Read 1 Overlap Overlap Overlap
- 16. Greedy extension Oldest http://www.cbcb.umd.edu/research/assembly_primer.shtml
- 17. Overlap-Layout-Consensus Typical for Sanger-type reads also used by newbler from 454 Life Sciences Steps Overlap computation Layout: graph simplification Consensus: sequence
- 18. Overlap-Layout-Consensus Overlap phase: K-mer seeds initiate overlap ACGCGATTCAGGTTACCACG
- 19. de Bruijn graphs Developed outside of DNA-related work Best solution for very short reads ≤100 nt GACCTACA GAC ACC CCT CTA TAC ACA Read de Bruijn graph K-mers (K=3) K-1 bases overlap
- 20. Graphs Schatz M C et al. Genome Res. 2010;20:1165-1173
- 21. Graphs Simplify the graph Add scaffolding information
- 22. Sequence data Sequencing errors add complexity to graph create new k-mers Correction of errors k-mer frequency Kelley et al.Genome Biology 2010 11:R116
- 23. How to sequence a genome human 1990's cod 1 2009 - 2011 cod 2 2011 - 2012
- 24. Human genome Public effort BAC-by-BAC sequencing hierarchical shotgun sequencing Genome BACs Select BACs 100-150 kb shotgun sequencing http://www.cbcb.umd.edu/research/assembly_primer.shtml
- 25. Human genome Celera: shotgun sequencing entire genome shotgun use of mate pairs
- 26. How to sequence a genome Preparations BAC-by-BAC Add shotgun and mate pairs
- 27. The cod genome project Preparations * From a different individual
- 28. Cod: strategy ‘454 only’ NO subcloning Pure ‘shotgun’ approach 454 specific paired end libraries Supplementary BAC ends using Sanger sequencing
- 29. Cod: sequencing
- 30. Cod: assembly Input for assembly 84 million reads 28 billion bases (Gb) 34x coverage Assembly program Newbler from 454 Celera from Venter Inst. Computing nodes 24 cpus 128 GB of memory
- 31. Cod: assembly 611 Mb in 6 467 scaffolds but 35% gap bases short contigs incomplete genes
- 32. Cod: gaps Polymorphiccontig 2 Heterozygosity Contig 4 Contig 1 Polymorphiccontig 3 Short Tandem Repeats ACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA ACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA ACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA ACACACACACACACACACACACACACACACACACACACACACACACACACACACACACA
- 33. Cod: annotation Ensembl 'repair' genes based on stickleback sequence ~22 000 genes http://pre.ensembl.org/Gadus_morhua/
- 35. Cod 2: 2011-2012 Close the gaps increase contig size Pseudochromosomes genetic linkage map scaffolds to 'chromosomes' anchoring ordering and orienting
- 36. Cod 2: strategy New data Illumina reads longer 454 reads ~700 bases PacBio reads? Improved programs newbler New programs assembly gap closing
- 37. Many programs to choose from
- 38. Assembly competitions Assemblathon 1 simulated datasets ALLPATHS_LG – Broad Institute MIT (US) Soapdenovo – BGI (China) SGA – Sanger Institute (UK)
- 39. Assembly competitions Assemblathon 2 real datasets snake – Illumina only cichlid fish – Illumina only parrot Illumina 454 FLX+ PacBio http://assemblathon.org/
- 40. How to sequence a genome In 2011 Cheap alternative: RAD-tag sequencing
- 41. How to sequence a genome Foundation of Illumina data 100x coverage Paired End reads (2x100bp) several Mate Pair libraries 2kb, 3kb, 8k, 10kb, bigger? this is now very cheap! Fill gaps with long reads 454 or PacBio
- 42. How to sequence a genome Add lots of bioinformatics... http://cores.montana.edu/index.php?page=bioinformatics-core-facility
- 43. Thank you! lex.nederbragt@bio.uio.no www.sequencing.uio.no www.sequencing.uio.no
Editor's Notes
- Greedy assemblers - The first assembly programs followed a simple but effective strategy in which the assembler greedily joins together the reads that are most similar to each other. An example is shown in Figure 8, where the assembler joins, in order, reads 1 and 2 (overlap = 200 bp), then reads 3 and 4 (overlap = 150 bp), then reads 2 and 3 (overlap = 50 bp) thereby creating a single contig from the four reads provided in the input. One disadvantage of the simple greedy approach is that because local information is considered at each step, the assembler can be easily confused by complex repeats, leading to mis-assemblies.
- BAC-by-BAC approach. The long lines represent individual BACs. The minimal tiling path is represented by thick lines. Each BAC in the tiling path is then sequenced through the shotgun method.