Visualizing the Structural Variome (VMLS-Eurovis 2013)

712 views

Published on

Published in: Technology, Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
712
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Visualizing the Structural Variome (VMLS-Eurovis 2013)

  1. 1. Visualizing theStructural VariomeProf Jan AertsBiological Data Management and VisualizationBioinformatics/iMinds - University of Leuven, Belgiumjan.aerts@esat.kuleuven.be@jandot - http://orcid.org/0000-0002-6416-2717
  2. 2. Visualizing theStructural VariomeProf Jan AertsBiological Data Management and VisualizationBioinformatics/iMinds - University of Leuven, Belgiumjan.aerts@esat.kuleuven.be@jandot - http://orcid.org/0000-0002-6416-2717Genomic
  3. 3. Genetic dogma
  4. 4. Genomic variation
  5. 5. • single nucleotide polymorphisms (SNPs)• structural variation
  6. 6. What is the structural variome?
  7. 7. “copy number variation”
  8. 8. • effect on phenotype through• change in abundance of mRNA and proteins• disrupted genes: partly deleted, fusion genes, ...
  9. 9. Why do we care?
  10. 10. • 12% of genome is covered by copy number variable regions• colour vision in primates• CCL3L1 copy number -> susceptibility to HIV• AMY1 copy number -> diet (starch digestion)=> “the dynamic genome”
  11. 11. • Chromosome fusion great apes• Cancerhttp://bit.ly/11wamow http://bit.ly/14Xnwglhttp://bit.ly/11WyzEB
  12. 12. • Embryogenesis• Down SyndromeRobberecht et al, Current Genomics, 2010Le Huitième Jourhttp://bit.ly/14Xrypa
  13. 13. Visualization for1. discovery2. interpretation/diagnosis
  14. 14. 1. Discovery of structural variation
  15. 15. 1. karyotyping, fluorescent in situ hybridization2. array comparative genome hybridization (aCGH): Manhattan plotFeuk, Nature Reviews Genetics, 2006Xie & Tammi, BMC Bioinformatics, 2009
  16. 16. 3. next-generation DNA sequencing (NGS), based on: read-depth, read-pairs,split reads, local assemblya. read-depth information: =~ aCGH
  17. 17. b. read-pair information: identify signaturesMedvedev, Nature Methods, 2009
  18. 18. • Integration of read-depth and read-pair information at high resolution usingHilbert curves: MeanderPavlopoulos et al, Nucleic Acids Research, 2013=> used in single-cell sequencing projects
  19. 19. 2. Interpretation of structural variation ->diagnostics
  20. 20. • linearity of reference chromosome broken by structural variation, but stillusing the reference for comparison• visualization of evidence, not effectUCSC Genome BrowserStephens et al, Cell, 2011
  21. 21. => both: domain expert needs to try and “wrap his head around” the dataHow can we help as visualization experts?• lessen the cognitive load in interpretation: change a cognitive into aperceptual one
  22. 22. • Our lab: dual approach1. focus on functional impact - PipitSakai et al, submitted
  23. 23. 2. represent the chromosome as it is in vivo (=~ FISH)reconstruct rearranged chromosome based on graph structure of segments
  24. 24. • Other future work• analysis/visualization of single-molecule DNA sequencing data (e.g.towards single-cell sequencing)• scalable analysis/visualization in omics: how can we develop methods forcomparing the genomes of 1,000s of individuals?• cross-omic data integration (genome, transcriptome, proteome,metabolome, ...) => molecular quantified self

×