Alper Sarikaya 1, Michael Correll 2, Jorge M. Dinis 1, David H. O Connor 1,3, and Michael Gleicher 1

Transcription

1 Alper Sarikaya 1, Michael Correll 2, Jorge M. Dinis 1, David H. O Connor 1,3, and Michael Gleicher 1 1 University of Wisconsin-Madison 2 University of Washington 3 Wisconsin National Primate

2 Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work

3 RNA viruses are very error prone in replication Viruses accumulate variation to help its survival Influenza, H1N1, Zika are hard to eliminate

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5 Discover where functional shifts are occurring

6 Identify co-occurrences of mutations in genome

7 Identify groups of like-behaving subpopulations

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11 Identify pairs of positions where mutations co-occur Analysis requires a maximum of sifting through (# positions) 2 correlations

12 Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work

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21 Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work

22 Collect counts of bases (A, C, T, G) for each pair of positions

23 Compute co-occurrence strength between every pair of genomic positions

24 Overview Super-zoom Show co-occurrences in full pairwise genomic space, in a web browser Scale up to 20,000 x 20,000 Color shows the co-occurrence strength Pairwise genomic space Key

25 Show co-occurrences in full pairwise genomic space, in a web browser Scale up to 20,000 x 20,000 Color shows the co-occurrence strength

26 Too much data to sift through Alignment errors produce false positives Difficult to get an overview

27 Always present data in genomic sequence order Display annotations alongside genome Scaffold to navigate space of all pairwise correlation Support identifying synonymy

28 Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work

29 Coverage (read depth) Variation (mutations) Co-occurrence strength

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31 User-controlled metrics

32 Annotations Positions with significant co-occurrences

33 Pairwise co-occurrences with a particular position

34 Reads that do not overlap with the paired position

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37 Biological Background Displaying occurrence relationships (in biology) MatrixViewer CooccurViewer Case Study, Future Work

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

39 Use analyst-controlled metrics to focus exploration Displaying the full space does not necessarily empower analysts Providing usable context and scaffolding

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

41 @yelperalp Funding from the NIH and NSF Feedback from colleagues, virologists, and reviewers Code and working demo available online!