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

3. Genetic dogma

4. Genomic variation

5. • single nucleotide polymorphisms (SNPs)
• structural variation

6. What is the structural variome?

8. “copy number variation”

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