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Visualization of 3D Genome Data
Nils Gehlenborg presented on visualizing 3D genome data. He discussed how Hi-C data captures chromosomal interactions to measure 3D genome structure, but results in massive interaction matrices. Existing tools have limitations in scale, comparison across conditions, and navigation. Gehlenborg demonstrated HiGlass, an interactive multi-resolution viewer for exploring these matrices. He also introduced HiPiler for investigating detected patterns, by filtering, aggregating, and correlating them with other data. HiGlass and HiPiler address challenges of visualizing 3D genome data at different scales and across many samples.
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Visualization of 3D Genome Data
- 1. Nils Gehlenborg, PhD http://gehlenborglab.org Visualizationof 3D Genome Data HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS @ngehlenborg
- 2. DNA in theNucleus
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- 5. Dekker et al.,Nature, 2017
- 6. Why is 3DGenome Data interesting?
- 7. Role of 3DDNA Structure & Dynamics Cell Division Gene Regulation Structural Variation Dekker et al., Nature, 2017
- 8. Cell Division Gene Regulation Enhancers:spatial proximity to control gene expression Clustering of chromatin near lamina: gene silencing GWAS: many variants found in non-coding regions Structural Variation Dekker et al., Nature, 2017
- 9. Cell Division Gene Regulation StructuralVariation Spatial arrangement influences structural variation Dekker et al., Nature, 2017
- 10. Role of 3DDNA Structure & Dynamics Cell Division Gene Regulation Structural Variation Dekker et al., Nature, 2017
- 11. How do wemeasure chromosomal conformation?
- 12. De Wit andDe Laat, Genes & Development, 2012 Chromosome Conformation Capture
- 13. De Wit andDe Laat, Genes & Development, 2012
- 14. De Wit andDe Laat, Genes & Development, 2012
- 15. Rao et al.,Cell, 2014 Hi-C Protocol
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- 17. How big isa Hi-C Interaction Matrix?
- 18. 3,000,000 x 3,000,000 pixels Printedat 300 DPI ~250 x 250 meters ~830 x 830 feet How big is a Hi-C Interaction Matrix? Typical resolution today: Reads mapped into 1,000 bp bins → ~3,000,000 x 3,000,000 matrix for a whole genome Printed at 300 DPI ~250 x 250 meters ~830 x 830 feet
- 19. 3,000,000 x 3,000,000 pixels Printedat 300 DPI ~250 x 250 meters ~830 x 830 feet By Sam valadi - https://www.flickr.com/photos/132084522@N05/17178926219/in/photostream/
- 20. 3,000,000 x 3,000,000 pixels Printedat 300 DPI ~250 x 250 meters ~830 x 830 feet We need a bigger screen!
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- 22. What are somevisualization challenges involved?
- 23. 1. View interactionsat different scales From genome to individual bins 2. Compare interactions across many conditions Two or more conditions 3. View and compare features Within and across maps 4. Navigating an enormous data space With few well known landmarks 5. Do all of this in a web browser Interaction and low latency
- 24. How can wevisualize the data?
- 25. Network Visualization 1. (3Mx 3M)/2 interactions 2. Weight for each interaction 3. Constraint: nodes in sequence order
- 26. MatrixNode-Link Diagram Network Visualization 1. (3Mx 3M)/2 interactions 2. Weight for each interaction 3. Constraint: nodes in sequence order
- 27. Genome Interaction DataVisualization Scale 1. Global Interactions (whole chromosome or genome) 2. Local Interactions (immediate feature neighborhood) 3. Individual Features Encoding 1. Heatmap 2. Node-Link Diagram (here: Arc Diagram) 3. 3D
- 28. Genome Interaction DataVisualization Scale 1. Global Interactions (whole chromosome or genome) 2. Local Interactions (immediate feature neighborhood) 3. Individual Features Encoding 1. Heatmap 2. Node-Link Diagram (here: Arc Diagram) 3. 3D Illustration of concepts and models!
- 29. Genome Interaction DataVisualization
- 30. Reviewed in Yardımcı& Noble, Genome Biology, 2017, http://aidenlab.org/juicebox/ Global Interactions Juicebox HEATMAP Caveat only qualitative interpretation of color map possible
- 31. Wong 2010, NatureMethods & https://en.wikipedia.org/w/index.php?curid=45522095 Mini Excursion: Color Color is a relative medium!
- 32. Reviewed in Yardımcı& Noble, Genome Biology, 2017, http://aidenlab.org/juicebox/ Global Interactions Juicebox HEATMAP Caveat only qualitative interpretation of color map possible
- 33. DOI 10.1101/121889, http://higlass.io,Kerpedjiev, Abdennur, Lekschas …, Mirny, Park, Gehlenborg Global Interactions HiGlass HEATMAP Caveat only qualitative interpretation of color map possible
- 34. Reviewed in Yardımcı& Noble, Genome Biology, 2017, http://epigenomegateway.wustl.edu/ Global Interactions Washington University Epigenome Browser ARC DIAGRAM Caveats line crossings, limited dynamic range zooming complex
- 35. http://rondo.ws, O’Donoghue Lab GlobalInteractions Rondo ARC DIAGRAM Caveats line crossings, limited dynamic range
- 36. http://rondo.ws, O’Donoghue Lab GlobalInteractions Rondo ARC DIAGRAM Caveat line crossings, colors hard to map
- 37. http://promoter.bx.psu.edu/hi-c/, Reviewed inYardımcı & Noble, Genome Biology, 2017 Local Interactions 3D Genome Browser HEATSTRIP Caveat height of triangle grows with distance of interaction
- 38. Reviewed in Yardımcı& Noble, Genome Biology, 2017, http://epigenomegateway.wustl.edu/ Local Interactions Washington University Epigenome Browser ARC DIAGRAM Caveats zooming is problematic, no context
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- 40. http://higlass.io/app/?config=TKXaqsSIRvGEcw2dAUQvxg 2D Maps Build aHi-C Interaction Map Viewer
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- 42. 1D Tracks Build aGenome Browser
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- 44. Prioritization Orient Users inthe Visualization
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- 48. Linked Views Support Overviewand Detail
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- 50. Linked Views++ Support Explorationand Analysis
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- 52. Example 1a Schwarzer etal. Nature, 2017
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- 54. Example 1b Schwarzer etal. Nature, 2017
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- 56. Example 2 Forcato etal. Nature Methods, 2017
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- 58. Many pattern instancesbut sparse distribution! How can we explore and compare many local patterns in this very large matrix?
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- 60. Challenges • Detected byalgorithms • Occur frequently • "Noisy" results Goals • Quality assessment • Pattern stratification • Pattern correlation Points Blocks
- 61. • How dospecific pattern or average pattern look? • How variable and noisy are detected patterns? • Are there subgroups among the pattern? • How are patterns related to other data attributes? • What does the patterns neighborhood look like?
- 62. TECHNIQUES? • Pan &Zoom Kerpedjiev et al.: HiGlass • Lenses / Multifocus Rao and Card: Table Lens Elmquist et al.: Melange • Abstraction / Aggregation Dunne et al.: Motif Simplification Elmquist et al.: ZAME • Small Multiples Bach et al.: Multipiles
- 63. Cut the Matrixinto Pieces!
- 64. Cut the Matrixinto Pieces!
- 65. Cut the Matrixinto Pieces!
- 66. Cut the Matrixinto Pieces!
- 67. Cut the Matrixinto Pieces!
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- 74. 1. FILTERING Assess quality& separate signal from noise
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- 80. 2. AGGREGATE Stratify patternsand assess pattern variability
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- 85. 3. CONTEXT Correlate patternswith each another & other pattern types
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- 89. Pile Inspection Attributecorrelations Multidimensional Clustering Dataset Comparison
- 90. HiGlass HiPiler Investigate local andglobal interactions Small number of features at a time Strong focus on local context Investigate features across the whole map View hundreds or thousands of features at a time Weak support for context
- 91. HiGlass HiPiler Investigate local andglobal interactions Small number of features at a time Strong focus on local context Investigate features across the whole map View hundreds or thousands of features at a time Weak support for context ?
- 92. HiGlass HiPiler Investigate local andglobal interactions Small number of features at a time Strong focus on local context Investigate features across the whole map View hundreds or thousands of features at a time Weak support for context Dynamic Aggregatable Insets
- 93. Dynamic Aggregatable Insets Lekschaset al., Work in Progress
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- 95. Open Challenges Integration withImaging Data Multi contact data Single cell data Visualization of temporal dynamics
- 96. Acknowledgements Peter Kerpedjiev, PhDFritz Lekschas, MSc HARVARD MEDICAL SCHOOL HARVARD SCHOOL OF ENGINEERING & APPLIED SCIENCES Funding provided by NIH COMMON FUND (U01 CA200059) NIH NATIONAL HUMAN GENOME RESEARCH INSTITUTE (R00 HG007583)
- 97. Acknowledgements Peter Kerpedjiev Fritz Lekschas NezarAbdennur Benjamin Bach Chuck McCallum Kasper Dinkla Hendrik Strobelt Jacob M Luber Scott B Ouellette Alaleh Ahzir Nikhil Kumar Jeewon Hwang Danielle Nguyen Burak H Alver Job Dekker Hanspeter Pfister Leonid A Mirny Peter J Park
- 98. Nils Gehlenborg, PhD http://gehlenborglab.org Visualizationof 3D Genome Data HARVARD MEDICAL SCHOOL DEPARTMENT OF BIOMEDICAL INFORMATICS @ngehlenborg
- 99. Tools Demo Site: http://higlass.io Code:https://github.com/hms-dbmi/higlass Docker: https://hub.docker.com/r/gehlenborglab/higlass/ Preprint: https://doi.org/10.1101/121889 HIGLASS HIPILER Demo Site: http://hipiler.higlass.io Code: https://github.com/flekschas/hipiler Preprint: https://doi.org/10.1101/123588 Paper: http://doi.org/10.1109/TVCG.2017.2745978 IEEE TVCG (2018)