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Zemin Ning The Wellcome Trust Sanger Institute

Assembly Scaffolding using String Graphs and In Silico Chromosome Assignment. Zemin Ning The Wellcome Trust Sanger Institute. Phusion2 Assembly Pipeline. Assembly. Illumina Reads. Supercontig. AGPcontig. Contigs. 2x75 or 2x100bp. Flow-sorting Reads Map Markers. Data Process.

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Zemin Ning The Wellcome Trust Sanger Institute

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  1. Assembly Scaffolding using String Graphs and In Silico Chromosome Assignment Zemin Ning The Wellcome Trust Sanger Institute

  2. Phusion2 Assembly Pipeline Assembly Illumina Reads Supercontig AGPcontig Contigs 2x75 or 2x100bp Flow-sorting Reads Map Markers Data Process Mate Pair Reads BAC Ends Base Correction Consensus Generation Reads Group

  3. Spinner – a scaffolding tool Spinner uses mate pair data to scaffold contigs. Contigs, and pairs of contigs connected by pairs, define a bi-directional graph: Using expected insert size, a estimate of the gap size can be given for each contig.

  4. Spinner – removing bad pairs Spinner seeks to delete spurious connections where possible. Pairs screened for (a) PCR duplication, (b) cross-biotin and (c) chimeric pairs, etc.  Max insert length If placement of reads implies a large negative distance between the contigs, pair is discarded.  Max insert length After merging two contigs… this check is repeated to find more spurious pairs.

  5. Spinner – deciding when to merge Connection to X with smallest gap size is merged -- as long as neither of these “conflicts” occur: A X B (1) According to the gap distance estimates and contig length, some alternative B overlaps A. A X B (2) Some alternative B is NOT connected to A. Must ALSO check the reverse: that there is nothing closer to A than X (and no conflicts with X from A). Conflicts may be resolved by a “strength comparison”.

  6. Spinner – still to do These techniques alone produces useful results. Further stages will be used to resolve repeats pairs that “jump over” repeats, and graph flow concepts.

  7. Remove Heterozygosity Contigs

  8. Pipeline of Contig Gap Closure

  9. Scaffold Comparisons SPINNER vs SSPACE SSPACESPINNER Genome_Size N50 AverageN50 Average Assemblathon 1 119 Mb 608Kb 86.8Kb 10Mb 450Kb Bamboo 2.0 Gb 322Kb 5804 488Kb 7689 Parrot 1.23 Gb 906Kb 4675 1.32Mb 6969

  10. Tasmanian tiger Tasmanian devil Australian Tasmanian

  11. Tasmanian devil facial tumour disease (DFTD) • Transmissible cancer characterised by the growth of large tumours on the face, neck and mouth of Tasmanian devils • Transmitted by biting • Commonly metastasises • First observed in 1996 • Primarily affects adults >1yr • Death in 4 – 6 months

  12. Tasmanian devil Tasmanian devil Wallaby Opossum

  13. Devil – Opossum Homology Map Based on Hybridisation Results of Devil Paints onto Opossum Chromosomes Opossum Devil 1 4 2a 3a 6 1 2 3 4 5 2b 5 3b X 6 7 8 X Opossum chromosome images were taken from Duke et a. 2007, Chromosome Res 15:361-370

  14. Genome size Flow cytometry analysis of chromosomal mixture of devil and opossum 3 2 1 1 Tasmanian devil 4 2 3 5 4 6 5+8 6 7 Opossum X X

  15. Table 1 Run ID, Template names, Number of reads and Chromosome size 4972_1 chr1 IL20_4972:1 19.8 571 4967_1 chr2 IL21_4967:1 20.0 610 4971_1 chr3 IL30_4971:1 21.7 556 4964_1 chr4 IL14_4964:1 7.26 450 4969_1 chr5 IL17_4969:1 7.06 341 4969_2 chr6 IL17_4969:2 8.59 277 4969_3 chrx IL17_4969:3 9.43 122 Read mapping coefficient: e = Size_of_Chr/Num_reads_in_lane

  16. Perfect - Reads from the same library were mapped to the contig

  17. Acceptable - Majority of the reads were from the same library, but there were reads from other libraries

  18. Bad – mis-assembly error Majority of the reads in one region were from one library. But there is a transition from which we see a new library, i.e. switch to another chromosome.

  19. Unassigned contigs were placed by supercontigs using mate pairs

  20. Scaffolds Assigned to Chromosomes using Flow-sorting Data Chr_ID Chr_size Scaffolds_assigned Bases_assigned Mb Chr1 571 6729 684 Chr2 610 8381 740 Chr3 556 7197 641 Chr4 450 4817 487 Chr5 341 3188 300 Chr6 277 2844 263 Chrx 122 2378 86.6 Unassigned 440 1.23

  21. Genome Assembly Normal – T. Devil Solexa reads: Number of read pairs: 650 Million;Estimated genome size: 3.1 GB; Read length: 2x100bp; Estimated read coverage: ~40X; Insert size: 410/50-600 bp; Mate pair data: 2k,4k,5k,6k,8k,10k Number of reads clustered: 591 Million Assembly features: - stats Contigs SupercontigsTotal number of contigs: 178,711 26,954 Total bases of contigs: 2.95 Gb 3.08 Gb N50 contig size: 28,921 2,244,460 Largest contig: 214,456 6,014,846 Averaged contig size: 16,511 114,451 Contig coverage on genome: ~94% >99% Ratio of placed PE reads: ~92% ?

  22. Devil Tumour Genome Assemblies Solexa reads: Tumour_87T Tumour_53T Number of read pairs: 760 Million 669 M;Finished genome size: 3.2 GB 3.2 GB; Read length: 2x100 2x100; Estimated read coverage: ~46X ~40X; Insert size: 300bp 300bp; Number of reads clustered: 635 Million 603 M Assembly features: - stats Tumour_87T Tumour_53TTotal number of contigs: 532,584 612,288 Total bases of contigs: 3.13 Gb 3.14 Gb N50 contig size: 15,908 14,632 Largest contig: 109,065 170,831 Averaged contig size: 5,882 5,567 Contig coverage on genome: ~95% ~95% Ratio of placed PE reads: ~92% ~92%

  23. DFTD1 K I F1 F F2 D G/H E F A M1 J M2? M3 1 der1 der2 3 4 5 der5 6 der6 M4 X 1 X 6 5 2 5 6 2 X? 5 X 2 2

  24. DFTD2 L M K3 J K1/K2 I D F G J H M2 M1 M3 der6 der5 der1 B 1 2 3 4 5 6 Xp Xq 5 1 6 2 2 1 X 2 X X 2 2

  25. Acknowledgements: • Joe Henson • Elizabeth Murchuson • David McBride • Yong Gu • Fengtang Yang • Mike Stratton • Ole Schulz-Trieglaff • Dirk Evers • David Bentley

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