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Visualizing the
Structural Variome
Prof Jan Aerts
Biological Data Management and Visualization
Bioinformatics/iMinds - University of Leuven, Belgium
jan.aerts@esat.kuleuven.be
@jandot - http://orcid.org/0000-0002-6416-2717
Visualizing the
Structural Variome
Prof Jan Aerts
Biological Data Management and Visualization
Bioinformatics/iMinds - University of Leuven, Belgium
jan.aerts@esat.kuleuven.be
@jandot - http://orcid.org/0000-0002-6416-2717
Genomic
Genetic dogma
Genomic variation
• single nucleotide polymorphisms (SNPs)
• structural variation
What is the structural variome?
Visualizing the Structural Variome (VMLS-Eurovis 2013)
“copy number variation”
• effect on phenotype through
• change in abundance of mRNA and proteins
• disrupted genes: partly deleted, fusion genes, ...
Why do we care?
• 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”
• Chromosome fusion great apes
• Cancer
http://bit.ly/11wamow http://bit.ly/14Xnwgl
http://bit.ly/11WyzEB
• Embryogenesis
• Down Syndrome
Robberecht et al, Current Genomics, 2010
Le Huitième Jour
http://bit.ly/14Xrypa
Visualization for
1. discovery
2. interpretation/diagnosis
1. Discovery of structural variation
1. karyotyping, fluorescent in situ hybridization
2. array comparative genome hybridization (aCGH): Manhattan plot
Feuk, Nature Reviews Genetics, 2006
Xie & Tammi, BMC Bioinformatics, 2009
3. next-generation DNA sequencing (NGS), based on: read-depth, read-pairs,
split reads, local assembly
a. read-depth information: =~ aCGH
b. read-pair information: identify signatures
Medvedev, Nature Methods, 2009
• Integration of read-depth and read-pair information at high resolution using
Hilbert curves: Meander
Pavlopoulos et al, Nucleic Acids Research, 2013
=> used in single-cell sequencing projects
2. Interpretation of structural variation ->
diagnostics
• linearity of reference chromosome broken by structural variation, but still
using the reference for comparison
• visualization of evidence, not effect
UCSC Genome Browser
Stephens et al, Cell, 2011
=> both: domain expert needs to try and “wrap his head around” the data
How can we help as visualization experts?
• lessen the cognitive load in interpretation: change a cognitive into a
perceptual one
• Our lab: dual approach
1. focus on functional impact - Pipit
Sakai et al, submitted
Visualizing the Structural Variome (VMLS-Eurovis 2013)
2. represent the chromosome as it is in vivo (=~ FISH)
reconstruct rearranged chromosome based on graph structure of segments
• 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 for
comparing the genomes of 1,000s of individuals?
• cross-omic data integration (genome, transcriptome, proteome,
metabolome, ...) => molecular quantified self

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Visualizing the Structural Variome (VMLS-Eurovis 2013)

  • 1. Visualizing the Structural Variome Prof Jan Aerts Biological Data Management and Visualization Bioinformatics/iMinds - University of Leuven, Belgium jan.aerts@esat.kuleuven.be @jandot - http://orcid.org/0000-0002-6416-2717
  • 2. Visualizing the Structural Variome Prof Jan Aerts Biological Data Management and Visualization Bioinformatics/iMinds - University of Leuven, Belgium jan.aerts@esat.kuleuven.be @jandot - http://orcid.org/0000-0002-6416-2717 Genomic
  • 5. • single nucleotide polymorphisms (SNPs) • structural variation
  • 6. What is the structural variome?
  • 9. • effect on phenotype through • change in abundance of mRNA and proteins • disrupted genes: partly deleted, fusion genes, ...
  • 10. Why do we care?
  • 11. • 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”
  • 12. • Chromosome fusion great apes • Cancer http://bit.ly/11wamow http://bit.ly/14Xnwgl http://bit.ly/11WyzEB
  • 13. • Embryogenesis • Down Syndrome Robberecht et al, Current Genomics, 2010 Le Huitième Jour http://bit.ly/14Xrypa
  • 14. Visualization for 1. discovery 2. interpretation/diagnosis
  • 15. 1. Discovery of structural variation
  • 16. 1. karyotyping, fluorescent in situ hybridization 2. array comparative genome hybridization (aCGH): Manhattan plot Feuk, Nature Reviews Genetics, 2006 Xie & Tammi, BMC Bioinformatics, 2009
  • 17. 3. next-generation DNA sequencing (NGS), based on: read-depth, read-pairs, split reads, local assembly a. read-depth information: =~ aCGH
  • 18. b. read-pair information: identify signatures Medvedev, Nature Methods, 2009
  • 19. • Integration of read-depth and read-pair information at high resolution using Hilbert curves: Meander Pavlopoulos et al, Nucleic Acids Research, 2013 => used in single-cell sequencing projects
  • 20. 2. Interpretation of structural variation -> diagnostics
  • 21. • linearity of reference chromosome broken by structural variation, but still using the reference for comparison • visualization of evidence, not effect UCSC Genome Browser Stephens et al, Cell, 2011
  • 22. => both: domain expert needs to try and “wrap his head around” the data How can we help as visualization experts? • lessen the cognitive load in interpretation: change a cognitive into a perceptual one
  • 23. • Our lab: dual approach 1. focus on functional impact - Pipit Sakai et al, submitted
  • 25. 2. represent the chromosome as it is in vivo (=~ FISH) reconstruct rearranged chromosome based on graph structure of segments
  • 26. • 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 for comparing the genomes of 1,000s of individuals? • cross-omic data integration (genome, transcriptome, proteome, metabolome, ...) => molecular quantified self