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Visualization Approaches for Biomedical Omics Data: Putting It All Together
The document discusses various visualization approaches for analyzing biomedical omics data, including genome, transcriptome, proteome, metabolome, and interactome. It emphasizes the importance of integrating different types of data for better insights into biological processes and clinical implications. The document also highlights the need for building infrastructure and tools for effective data visualization and management in biomedical research.
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Visualization Approaches for Biomedical Omics Data: Putting It All Together
- 1. Visualization Approaches for Biomedical Omics Data: Putting It All Together Nils Gehlenborg Harvard Medical School Center for Biomedical Informatics !nils_gehlenborg
- 2. Why am Idoing this?
- 3. Human " # Machine
- 4. Human " INTERPRETATION Data COMPUTATION GENERATION # Machine
- 5. “In every chainof reasoning, the evidence of the last conclusion can be no greater than that of the weakest link of the chain, whatever may be the strength of the rest.” - Thomas Reid, Essays on the Intellectual Powers of Man (1786)
- 6. Human " INTERPRETATION Data COMPUTATION GENERATION # Machine Hypotheses Discoveries Knowledge Cognition
- 7. x y1 y2 1.00 0.96 0.76 2.00 0.76 -0.14 3.00 -0.14 -0.91 4.00 -0.91 -0.84 5.00 -0.84 0.00 6.00 0.00 0.84 7.00 0.84 0.91 8.00 0.91 0.14 9.00 0.14 -0.76 10.00 -0.76 -0.96 11.00 -0.96 -0.28
- 8. 1 0.5 0 -0.5 -1 1 2 3 4 5 6 7 8 9 10 11
- 9. Human INTERPRETATION |Data COMPUTATION GENERATION Machine Hypotheses Discoveries Knowledge " # Cognition
- 10. What are thedata?
- 11.
- 13. DNA Icon byDarrin Higgins, from The Noun Project
- 14. Genome What isthe DNA sequence?
- 15. Genome HOW? sequencingof genomic DNA WHAT? single nucleotide variants (SNVs) copy number variants complex structural variants
- 16.
- 17.
- 18. Clark et al.2009, PLoS Genetics http://circos.ca, Krzywinski et al. 2009, Genome Research
- 19. source: Human destination:Lizard chr1 chr2 chr3 chr4 chr5 chr21 chr16 chr17 chr18 chr19 chr20 chr22 chrX chrY chr3 chr1 chr5 chrb chrd chrg chr15 chr14 chr4 chr13 chr2 chr12 chr6 chr3 chr11 chr7 chr10 chr8 chr9 chr6 chra chrc chrf chrh line saturation - + 10Mb chr3 go to: chr3 chr2 51280143 152008850 51709189 152345239 out in orientation: invert match inversion http://www.mizbee.org
- 20.
- 22. Genome Transcriptome Whatis the DNA sequence? Which genes are active?
- 23. Transcriptome HOW? sequencingof mRNA/microRNA molecules microarray-based hybridization WHAT? abundance of transcripts/genes/isoforms
- 24.
- 25.
- 27. Genome Transcriptome Proteome What is the DNA sequence? Which genes are active? Which proteins are present?
- 28. Proteome HOW? massspectrometry of peptides array-based techniques WHAT? presence of peptides & proteins abundance of peptides & proteins
- 30. Genome Transcriptome Proteome Metabolome What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified?
- 31. Metabolome HOW? massspectrometry NMR spectroscopy WHAT? presence of metabolites abundance of metabolites
- 32. Genome Transcriptome Proteome Metabolome What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 33. Interactome HOW? massspectrometry, yeast-2-hybrid text mining WHAT? links between molecules
- 34. Epigenome Genome Transcriptome Proteome Metabolome How are DNA and associated proteins modified? What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 35. Epigenome HOW? ChIP-seq,ChIP-chip (histones modifications) bisulfite sequencing (DNA methylation) WHAT? histone modifications along genome DNA methylation patterns along genome
- 36.
- 37.
- 38.
- 39. Nucleome Epigenome Genome Transcriptome Proteome Metabolome How is the DNA organized in space/time? How are DNA and associated proteins modified? What is the DNA sequence? Which genes are active? Which proteins are present? Which metabolites can be identified? Interactome Which molecules are interacting?
- 40. Nucleome HOW? 3C/4C/5Cchromosome conformation capture Hi-C sequencing WHAT? contact probabilities for different parts of the genome
- 41. Lieberman-Aiden et al.,Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome, 2009
- 42.
- 43. Dimensions Genomic Entities Data Types Samples Timepoints
- 44.
- 45. TCGA The CancerGenome Atlas
- 48. mRNA expression microRNAexpression DNA methylation protein expression copy number variants mutation calls clinical parameters
- 54.
- 55. Anthony92931 / WikimediaCommons + Modification by Nils Gehlenborg
- 58. StratomeX M Streit,A Lex, S Gratzl, C Partl, D Schmalstieg, H Pfister, PJ Park, N Gehlenborg, “Guided Visual Exploration of Genomic Stratifications in Cancer“, Nature Methods 11:884-885 (2014) A Lex, M Streit, H-J Schulz, C Partl, D Schmalstieg, PJ Park, N Gehlenborg, “StratomeX: Visual Anal-ysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization“, Computer Graphics Forum 31:1175-1184 (2012)
- 60. Is there amutation that overlaps with this mRNA cluster? Is there a mutually exclusive mutation? Is there a CNV that affects survival? Is there a pathway that is enriched in this cluster? Guided Exploration Query Rank Visualize Stratifications Clinical Params Pathways
- 62.
- 63.
- 64.
- 65. genome browser networkviewer heatmap visualization users visualization tools
- 66.
- 67. genome browser networkviewer heatmap visualization users visualization tools
- 68. users visualization tools customized tools for very specific problems
- 69. Meyer et al.,“MulteeSum: A Tool for Comparative Spatial and Temporal Gene Expression Data”, 2010
- 70. users visualization tools customized tools for very specific problems
- 71. What am Idoing?
- 72. Building infrastructure to build visualization tools.
- 73. Visualization Tools VisualizationComponents Visualization Libraries Data Analytics Data Management
- 74. Visualization Tools VisualizationComponents Visualization Libraries Data Analytics Data Management Abstraction
- 75. System/Platform Visualization Tools Visualization Components Visualization Libraries Data Analytics Data Management Abstraction
- 76. Think about scale. Think about systems.
- 77. Acknowledgements Harvard SEASAlexander Lex, Hanspeter Pfister MD Anderson Cancer Center University of Rostock Psalm Haseley, Richard Park, Peter J Park Michael S Noble, Douglas Voet, Lihua Zou, Spring Liu, Hailei Zhang, Sachet Shukla, Aaron McKenna, Andrew Cherniak, Pei Lin, Gad Getz Jianhua Zhang, Terrence Wu, Ian Watson, Steven Quayle, Lynda Chin Harvard Medical School Broad Institute of MIT & Harvard Graz University of Technology Christian Partl, Dieter Schmalstieg Johannes Kepler University Linz Samuel Gratzl, Stefan Luger, Marc Streit Hans-Jörg Schulz Harvard School of Public Health Funding NIH/NHGRI K99 HG007583 Ilya Sytchev, Shannan Ho Sui, Winston Hide