VIZBI 2014 - Visualizing Genomic Variation

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This talk was given at the VizBi 2014 conference. See vizbi.org/2014

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VIZBI 2014 - Visualizing Genomic Variation

  1. 1. Visualizing Genomic Variation Prof Jan Aerts Faculty of Engineering - ESAT/STADIUS iMinds Medical ICT Department KU Leuven ! jan.aerts@esat.kuleuven.be http://visualanalyticsleuven.be
  2. 2. What is genomic variation?
  3. 3. transitions transversions “copy number variation” Aerts & Tyler-Smith, In: Encyclopedia of Life Sciences, 2009
  4. 4. Effects of variation on phenotype • change in protein abundance • level of transcription or translation (loss/gain) • stability • change in protein structure (partly deleted, fusion genes, …)
  5. 5. What are we interested in? • multiple samples • • • show all affected genes (or functional units) cluster individuals functional effect of structural variation • • • gene-centric instead of positionally ordered: coordinate-free view high-level annotations (pathways, GO-terms) uncertainty (statistical & positional) and underlying evidence
  6. 6. DNA sequencing QC read mapping variant calling variant filtering what is effect of variant? check signal QC
  7. 7. Single Nucleotide Polymorphisms
  8. 8. General approach: reference-based
  9. 9. UCSC Ensembl
  10. 10. Variant View
 sequence variants in gene context Ferstay et al, IEEE InfoVis, 2013
  11. 11. Integrative Genome Viewer (IGV)
  12. 12. Sequence logo
  13. 13. Sequence Diversity Diagram
  14. 14. Structural Variation
  15. 15. dotplot Pevzner & Tessler, Genome Research, 2003
  16. 16. read depth information: arrayCGH and next-generation sequencing Xie & Tammi, BMC Bioinformatics, 2009
  17. 17. next-generation sequencing: read-pair information Medvedev, Nature Methods, 2009 Stephens et al, Cell, 2011
  18. 18. Integrate read-depth and read-pair information Stephens et al, Cell, 2010 Meander Pavlopoulos et al, Nucleic Acids Research, 2013
  19. 19. From data generation to data interpretation: understanding the effect of structural variation
  20. 20. linearity of reference chromosome broken by structural variation, but still using the reference for comparison ! ! UCSC Genome Browser => domain expert needs to try and “wrap his head around” the data => need to lessen the cognitive load in interpretation: change a cognitive task into a perceptual one
  21. 21. Nielsen & Wong, Nat Methods, 2012
  22. 22. represent the chromosome as it is in vivo (=~ FISH) Feuk, Nature Reviews Genetics, 2006 reconstruct rearranged chromosome based on graph structure of segments
  23. 23. breakpoint graph Pevzner & Tessler, Genome Research, 2003
  24. 24. focus on functional impact - Pipit Sakai et al, submitted
  25. 25. Challenges
  26. 26. Challenges • visual and interaction scalability • • deep sequencing => very high depth per track • high-dimensional data: many tracks (n=98!) • • genome size: HSA1 = 240Mb = 240,000 screens at 1pixel/bp = 72km compare multiple samples computational scalability • how to compute fast enough to make interactivity possible? (e.g. switching between data resolutions)
  27. 27. Thank you • Authors of papers mentioned • Bioinformatics/Visual Analytics Leuven • Ryo Sakai • Raf Winand • Thomas Boogaerts • Toni Verbeiren • Georgios Pavlopoulos • Data Visualization Lab (datavislab.org) • Erik Duval • Andrew Vande Moere 33
  28. 28. Questions?

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