This document discusses visualizing genomic variation from DNA sequencing data. It begins by defining genomic variation such as single nucleotide polymorphisms and structural variations. It then discusses analyzing multiple samples, showing affected genes and clustering individuals. The document outlines challenges in visualizing high-dimensional genomic data from deep sequencing at scale, while maintaining computational performance for interactivity. It proposes representing rearranged chromosomes based on segment relationships to focus on functional impacts.

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

3. What is genomic variation?

5. transitions
transversions
“copy number variation”
Aerts & Tyler-Smith, In: Encyclopedia of Life Sciences, 2009

6. 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, …)

7. What are we interested in?
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multiple samples
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•
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show all affected genes (or functional units)
cluster individuals
functional effect of structural variation
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•
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gene-centric instead of positionally ordered: coordinate-free view
high-level annotations (pathways, GO-terms)
uncertainty (statistical & positional) and underlying evidence

10. DNA sequencing
QC
read mapping
variant calling
variant filtering
what is effect of variant?
check signal
QC

11. Single Nucleotide Polymorphisms

12. General approach: reference-based

13. UCSC
Ensembl

15. Variant View
sequence variants in gene context
Ferstay et al, IEEE InfoVis, 2013

16. Integrative Genome Viewer (IGV)

17. Sequence
logo

18. Sequence Diversity Diagram

19. Structural Variation

20. dotplot
Pevzner & Tessler, Genome Research, 2003

21. read depth information: arrayCGH and next-generation sequencing
Xie & Tammi, BMC Bioinformatics, 2009

22. next-generation sequencing: read-pair information
Medvedev, Nature Methods, 2009
Stephens et al, Cell, 2011

23. Integrate read-depth and read-pair information
Stephens et al, Cell, 2010
Meander
Pavlopoulos et al, Nucleic Acids Research, 2013

24. From data generation to data interpretation:
understanding the effect of structural variation

25. 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

26. Nielsen & Wong, Nat Methods, 2012

27. represent the chromosome as it is in vivo (=~ FISH)
Feuk, Nature Reviews Genetics, 2006
reconstruct rearranged chromosome
based on graph structure of segments

28. breakpoint graph
Pevzner & Tessler, Genome Research, 2003

29. focus on functional impact - Pipit
Sakai et al, submitted

31. Challenges

32. Challenges
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visual and interaction scalability
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deep sequencing => very high depth per track
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high-dimensional data: many tracks (n=98!)
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genome size: HSA1 = 240Mb = 240,000 screens at 1pixel/bp = 72km
compare multiple samples
computational scalability
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how to compute fast enough to make interactivity possible? (e.g. switching
between data resolutions)

33. 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

34. Questions?