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New data from giab genomes strand-seq

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New data from giab genomes strand-seq

  1. 1. Peter Lansdorp Terry Fox Laboratory B.C. Cancer Research Centre Vancouver, Canada Strand-seq: a unique tool for genome analysis
  2. 2. Sequencing data Haplotypes Structural variation Genome assembly using short read data and bioinformatic approaches: a major challenge The promise of single molecule, long read approaches 4-5 million SNP’s per person: plenty to dig into
  3. 3. Parental haplotypes are scrambled in Genomic DNA Diluting DNA for library construction e.g. 10X.... DNA Haplotypes Structural variation
  4. 4. Is there a better way to assign haplotypes? 23 Chr 23 Chr Each cell has only one copy of each parental chromosome DNA Haplotypes Structural variation
  5. 5. Sequence DNA template strands in single cells: Strand-seq 23 Chr 23 Chr DNA Haplotypes Structural variation Is there a better way to assign haplotypes?
  6. 6. Identification of sister chromatids using Chromosome Orientation Fluorescence In situ Hybridization (CO-FISH) 5’ 3’ 3’ 5’ Cy3 labeled (TTAGGG)3 Cy5 labeled (CCCTAA)3 Ed Goodwin and Julianne Meyne Cytogenet Cell Genet. 63:126–7, 1993 Nature 463:93-7, 2010
  7. 7. Hoechst dye 33258 binds DNA with high affinity in the minor groove
  8. 8. 5’ 3’ 3’ 5’ Identification of sister chromatids using Chromosome Orientation Fluorescence In situ Hybridization (CO-FISH) Cy3 labeled (TTAGGG)3 Cy5 labeled (CCCTAA)3 Ed Goodwin and Julianne Meyne Cytogenet Cell Genet. 63:126–7, 1993 Nature 463:93-7, 2010
  9. 9. CO-FISH mouse ES cells 5’ end 3’ end Nature 463:93-7, 2010
  10. 10. CO-FISH mouse ES cells Major A rich Major T-rich Nature 463:93-7, 2010
  11. 11. Fluorescence activated cell sorting of single BrdU+ nuclei
  12. 12. Fluorescence activated cell sorting of single BrdU+ nuclei
  13. 13. Principle of Strand-Seq
  14. 14. Results of single cell Strand –seq on paired daughter cells
  15. 15. Strand-Seq reads currently covers only a few percent of the genome in a cell The number of reads that map to binned intervals of the reference genome varies around a constant average 9.34 4.144.75 Bin size = 200 kb 8 reads (150 bp) per 100 kb = less than 1% of the genome!
  16. 16. Sequencing of DNA template strands in single daughter cells using Strand-seq has many applications • mapping sister chromatid exchange events • de novo haplotype assembly • mapping polymorphic inversions • refining genome assemblies • study complex chromosomal abnormalities
  17. 17. Plus HSGV consortium paper Mark Hills et al., paper on genome assembly
  18. 18. best Strand-seq library thus far 16% or 0.16X David Porubsky Assembly of haplotyes using Strand-seq without studying parents or relying on linkeage data
  19. 19. Haplotype assembly using Strand-seq Strand-seq Hap-map reference
  20. 20. De novo haplotype assembly using Strand-seq allows mapping of parental meiotic recombination events
  21. 21. Chaisson et al., HGSVC Nat. Com., in press Strand-seq results
  22. 22. Chaisson et al., HGSVC in press
  23. 23. Strand-seq analysis of human cells reveals errors in the reference genome as well as polymorphic inversions Ashley Sanders
  24. 24. n = 23 n = 22 n = 20 n = 19 Homozygous Inversion Heterozygous Inversion Strand-seq analysis of human cells reveals errors in the reference genome and polymorphic inversions
  25. 25. Chaisson et al., HGSVC Nat. Com., in press Discovery of 156 inversions per genome, 58 intersecting with recurrent microdeletion and microduplication syndromes regions 22.9 Mb of inverted DNA per genome Compare: 5 million SNP’s per genome
  26. 26. Can we make better Strand-seq libraries by: 1.Reducing reaction volumes? 2.Avoid most DNA purification steps?
  27. 27. Funding: CIHR, NIH, ERC Ester Falconer Mark Hills Ashley Sanders David Porubsky Diana Spierings Adam Mattsson Zeid Hamadeh Vincent Hanlon Yanni Wang Daniel Chan AcknowledgementsAcknowledgements

Editor's Notes

  • But cumulatively, the translocation will always map to the same region, in libraries where it is visible. Here are all the chromosome 4 ideograms for 27 libraries in which the translocation is visible (we expect 50% of the libraries to show the translocation, by virtue of the random inheritance pattern of sister chromatids). You can see that the rearrangment maps to exactly the same region, shown by black arrowheads, in every library. This rearrangement is constant, while SCEs show up infrequently, and in different genomic locations in different libraries.
  • But cumulatively, the translocation will always map to the same region, in libraries where it is visible. Here are all the chromosome 4 ideograms for 27 libraries in which the translocation is visible (we expect 50% of the libraries to show the translocation, by virtue of the random inheritance pattern of sister chromatids). You can see that the rearrangment maps to exactly the same region, shown by black arrowheads, in every library. This rearrangement is constant, while SCEs show up infrequently, and in different genomic locations in different libraries.
  • Similarly, inversions can also be easily identified, as a small region that appears to be flanked by two SCE type switches. In a human cell line, we could easily identify this inversion on chromsome 8, in 100% of the libraries. This is a well-known polymorphic inversion on chr8, and is a relatively large region. However, much smaller inversions can also be identified, and we now have a bioinformatic pipeline in place to automate the identification of the human “invertome” in individual cells.

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