Published on March 7, 2014
Visualizing Genomic Variation Prof Jan Aerts Faculty of Engineering - ESAT/STADIUS iMinds Medical ICT Department KU Leuven ! firstname.lastname@example.org http://visualanalyticsleuven.be
What is genomic variation?
transitions transversions “copy number variation” Aerts & Tyler-Smith, In: Encyclopedia of Life Sciences, 2009
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, …)
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
DNA sequencing QC read mapping variant calling variant ﬁltering what is effect of variant? check signal QC
Single Nucleotide Polymorphisms
General approach: reference-based
Variant View sequence variants in gene context Ferstay et al, IEEE InfoVis, 2013
Integrative Genome Viewer (IGV)
Sequence Diversity Diagram
dotplot Pevzner & Tessler, Genome Research, 2003
read depth information: arrayCGH and next-generation sequencing Xie & Tammi, BMC Bioinformatics, 2009
next-generation sequencing: read-pair information Medvedev, Nature Methods, 2009 Stephens et al, Cell, 2011
Integrate read-depth and read-pair information Stephens et al, Cell, 2010 Meander Pavlopoulos et al, Nucleic Acids Research, 2013
From data generation to data interpretation: understanding the effect of structural variation
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
Nielsen & Wong, Nat Methods, 2012
represent the chromosome as it is in vivo (=~ FISH) Feuk, Nature Reviews Genetics, 2006 reconstruct rearranged chromosome based on graph structure of segments
breakpoint graph Pevzner & Tessler, Genome Research, 2003
focus on functional impact - Pipit Sakai et al, submitted
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)
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
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