SLIDE 1
Alper Sarikaya1, Michael Correll2, Jorge M. Dinis1, David H. O’Connor1,3, and Michael Gleicher1
1 University of Wisconsin-Madison 2 University of Washington 3 Wisconsin National Primate Center
Alper Sarikaya 1 , Michael Correll 2 , Jorge M. Dinis 1 , David H. - - PowerPoint PPT Presentation
Alper Sarikaya 1 , Michael Correll 2 , Jorge M. Dinis 1 , David H. - - PowerPoint PPT Presentation
Alper Sarikaya 1 , Michael Correll 2 , Jorge M. Dinis 1 , David H. OConnor 1,3 , and Michael Gleicher 1 1 University of Wisconsin-Madison 2 University of Washington 3 Wisconsin National Primate Center
SLIDE 2
SLIDE 3
RNA viruses are very error prone in replication Viruses accumulate variation to help its survival Influenza, H1N1, Zika are hard to eliminate
SLIDE 4
SLIDE 5
Discover where functional shifts are occurring
SLIDE 6
Identify ‘co-occurrences’ of mutations in genome
SLIDE 7
Identify groups of like-behaving subpopulations
SLIDE 8
SLIDE 9
SLIDE 10
SLIDE 11
Identify pairs of positions where mutations co-occur Analysis requires a maximum of sifting through (# positions)2 correlations
SLIDE 12
Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work
SLIDE 13
SLIDE 14
SLIDE 15
SLIDE 16
SLIDE 17
SLIDE 18
SLIDE 19
SLIDE 20
SLIDE 21
Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work
SLIDE 22
Collect counts of bases (A, C, T, G) for each pair of positions
SLIDE 23
Compute co-occurrence strength between every pair of genomic positions
SLIDE 24
Color shows the co-occurrence strength Show co-occurrences in full pairwise genomic space, in a web browser Scale up to 20,000 x 20,000
Overview Super-zoom Key Pairwise genomic space
SLIDE 25
Color shows the co-occurrence strength Show co-occurrences in full pairwise genomic space, in a web browser Scale up to 20,000 x 20,000
SLIDE 26
Too much data to sift through Alignment errors produce false positives Difficult to get an overview
SLIDE 27
Always present data in genomic sequence order Display annotations alongside genome Scaffold to navigate space of all pairwise correlation Support identifying synonymy
SLIDE 28
Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work
SLIDE 29
Coverage (read depth) Variation (mutations) Co-occurrence strength
SLIDE 30
http://graphics.cs.wisc.edu/Vis/CooccurViewer
SLIDE 31
http://graphics.cs.wisc.edu/Vis/CooccurViewer User-controlled metrics
SLIDE 32
Annotations
Positions with significant co-occurrences http://graphics.cs.wisc.edu/Vis/CooccurViewer
SLIDE 33
http://graphics.cs.wisc.edu/Vis/CooccurViewer Pairwise co-occurrences with a particular position
SLIDE 34
Reads that do not overlap with the paired position
SLIDE 35
SLIDE 36
SLIDE 37
Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work
SLIDE 38
Sample of : simian equivalent of HIV Large cluster of correlated mutations in Nef protein to evade T cell recognition Nearly no co-occurrences in structural proteins Gal & Pol
SLIDE 39
Use analyst-controlled metrics to focus exploration Displaying the full space does not necessarily empower analysts Providing usable context and scaffolding
SLIDE 40
Support comparison between multiple samples, and multi-step co-occurrence Data aggregation and filtering techniques to support larger data sizes Application to other event-driven sequences
SLIDE 41