Visually Exploring the Genome At Scale

INFO+ 2018 Talk: Supplementary Information

The human genome is about 2 meters long and tightly folded into the cell nucleus, a sphere that is 4 million times smaller than a pinhead. How do cells avoid entangling the DNA and ensure accessibility of necessary parts? Biologists study DNA folding through the detection of pairwise physical interactions along the DNA, which results in a 3-by-3 million pixel matrix. Visualized as a heatmap, thousands of local visual patterns become apparent. Studying these patterns is like trying to understand the average layout of parks while viewing countries on a world map. Biologists need to inspect these patterns for sensemaking of biological features. We have developed 3 interactive tools to explore such large datasets at different steps: (1) seamless browsing using HiGlass, (2) local pattern exploration through decomposition in HiPiler, and (3) guided navigation with Scalable Insets. I will present our tools and discuss the generalizability of the underlying concepts.

Presentation

Video coming soon

Tools

Publications

  1. HiGlass: web-based visual exploration and analysis of genome interaction maps

    1. Peter Kerpedjiev
    2. Nezar Abdennur
    3. Fritz Lekschas
    4. Chuck McCallum
    5. Kasper Dinkla
    6. Hendrik Strobelt
    7. Jacob M. Luber
    8. Scott B. Ouellette
    9. Alaleh Azhir
    10. Nikhil Kumar
    11. Jeewon Hwang
    12. Soohyun Lee
    13. Burak H. Alver
    14. Hanspeter Pfister
    15. Leonid A. Mirny
    16. Peter J. Park
    17. Nils Gehlenborg
    Genome Biology, 2018, 19:125. doi: 10.1186/s13059-018-1486-1
  2. HiPiler: Visual Exploration Of Large Genome Interaction Matrices With Interactive Small Multiples

    1. Fritz Lekschas
    2. Benjamin Bach
    3. Peter Kerpedjiev
    4. Nils Gehlenborg
    5. Hanspeter Pfister
    IEEE Transactions on Visualization and Computer Graphics (InfoVis), 24, 1, 522-531, 2018. doi: 10.1109/TVCG.2017.2745978
  3. Pattern-Driven Navigation in 2D Multiscale Visual Spaces with Scalable Insets

    1. Fritz Lekschas
    2. Michael Behrisch
    3. Benjamin Bach
    4. Peter Kerpedjiev
    5. Nils Gehlenborg
    6. Hanspeter Pfister
    bioRxiv, April 15, 2018. doi: 10.1101/301036 Preprint

Authors

  1. Fritz Lekschas

  2. Benjamin Bach

  3. Peter Kerpedjiev

  4. Michael Behrisch

  5. Nils Gehlenborg

  6. Hanspeter Pfister