Data Visualization And Annotation Workshop at Biocuration 2015
8th International Biocuration Conference. Beijing, China. April 23-26, 2015. Workshop 2: Data Visualization and Annotation. Chairs: Rama Balakrishnan, Stanford University, USA and Monica Munoz-Torres, Lawrence Berkeley National Laboratory, USA Explaining the most intricate biological processes often requires a degree of detail beyond the scope of equations and algorithms; in fact, most biological knowledge is represented visually as illustrations, graphs, and diagrams. Genomics data in particular require specialized forms of visualization to improve our understanding and increase our chances of extracting meaningful conclusions from our analyses. Furthermore, the heterogeneity and abundance of genomic data include widely varied sources, techniques for their obtention, and intrinsic experimental error. And even data obtained under similar conditions from two or more individuals are loaded with biological variation. So what is the best way to interpret the stories the data are telling us? Given the questions we wish to answer and the data we are generating, which tools would be most useful and effective? In this workshop we will explore the tools available for human interpretation of genomic data, specifically in the context of annotation. Presentations and perspectives, panelists/presenters: - Lorna Richardson, IGMM, University of Edinburgh, United Kingdom - Justyna Szostak, PMI Research & Development, Switzerland The workshop included a brief introduction to a landscape of tools available - as updated as the constantly changing field allows-, brief presentations chosen from abstract submissions and invited speakers, as well as ample discussion to capture the contributions and questions from attendants. In the end, we hope participants walked away with a toolset in hand that may benefit the progress of their own research.
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Data Visualization And Annotation Workshop at Biocuration 2015
- 1. Data Visualiza+on & Annota+on 8th Interna+onal Biocura+on Conference 24 April 2015 | Beijing, China Rama Balakrishnan Saccharomyces Genome Database Gene Ontology Consor5um Stanford University, CA, USA Image by Mar5n Krzywinski. Lim et al. Genome Biol (2015) 16:18 Monica Munoz-‐Torres Berkeley Bioinforma5cs Open-‐Source Projects Lawrence Berkeley Na5onal Lab, CA, USA
- 2. Outline 1. Introduc5on – Goals – Examples of genome visualiza5on tools 2. Panelists – Lorna Richardson: eMouseAtlas and Image Informa5cs – Justyna Szostak: Curated Causal Biological Network Models 3. Discussion – Featuring you! 2 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 2
- 3. 3 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference Goals of the workshop 1. To learn about tools available for human interpreta5on of genomic data, specifically in the context of annota5on. 2. To open a space for discussion: genomic data are ever more abundant and heterogeneous, with widely varied sources, produc5on techniques, and intrinsic experimental error. – How do we analyze these data? – What is the best way to interpret the stories the data are telling us? – How to put these together (overlay) visually? – Developers: what is the best way to disseminate and contribute code to make tool development easier?
- 4. Then and Now Figures 49 Tables 27 References 452 4 Figures 1 Tables 0 References 6 4
- 5. Genomic Data: Heterogeneous & Abundant • Structural: gene models, transcriptomes, RNAseq, differen5al expression, etc. • Func5onal: gene ontology, interac5ons, phenotypes, SNPs, complexes, protein abundance, diseases, images, etc. • Some examples . . . 5 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 5
- 7. Gene structure, ideograms, maps 7
- 8. Most of the curated data is text Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 8
- 9. Visualizing interac5on data Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 9
- 10. Overlaying curated data hgp://string-‐db.org
- 11. Complexes, interac5ons, and more Complex SGD_GO:0005955 calcineurin complex subunits Interac5ons hgp://3drepertoire.russelllab.org/ Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 11
- 12. Phenotypes and diseases hgp://monarchini5a5ve.org/ Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 12
- 13. Sequence varia5ons Sequence varia5on in various strains of S. cerevisiae 13
- 14. Molecular Model Edi5ng Environment Noctua – prototype from GOC -‐ Each node (box) is a func5on or process. -‐ Other nodes are folded in as OWL expressions. -‐ Users may add and drag elements -‐ Supports real 5me collabora5on 14
- 15. Understanding the Data Much of the interpreta+on requires human judgment. Visualiza+on improves our understanding and increases our chances of extrac+ng meaningful conclusions. 15 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 15
- 16. Cancer miRNome revealed survival differences in diffuse large B-‐ cell lymphoma pa5ents 16 Lim et al. Genome Biol 16:18 (2015)
- 17. Circos 17 ENCODE Circular Genome Data Visualiza+on -‐ Human placenta transcriptome -‐ Pancrea5c expression db -‐ Wall-‐sized High-‐res display for compara5ve analys. of CNV -‐ Chromosomal transloca5ons -‐ Variant iden5fica5on in mul5ple sclerosis -‐ Sorghum seedling development under Low Temp condi5ons -‐ Etc., etc., etc…
- 18. 18
- 19. Visualizing sequencing data 19 Nielsen et al. Nature Met Suppl 7: 3s. (2010) Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference
- 20. Genome Browsers 20 Nielsen et al. Nature Met Suppl 7: 3s. (2010) Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference
- 21. Compara5ve Genomics Visualiza5on 21 Nielsen et al. Nature Met Suppl 7: 3s. (2010) Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference
- 22. Communica5ng Complex Data Focus on meaning instead of structure—anchor the figure to relevant biology rather than to methodological details. 1) What are the interes5ng findings, and what representa5on would communicate them clearly? 22 2) Forgo conven5onal approaches to displaying mul5dimensional data. Beger to project the data onto familiar visual paradigms, such as a protein network or pathway, to saliently show biological effects in a func5onal context. Krzywinski and Savig. Nature Methods 10:7, 595 (2013)
- 23. Storytelling • Relate your data using the age-‐old custom of telling a story. – Stories have the capacity to delight and surprise and to spark crea5vity by making meaningful connec5ons between data and the ideas, interests and lives of your readers. 23 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference 23
- 24. Open-‐source: dissemina5on & contribu5ons • Gene5c & genomic informa5on is more valuable when shared • Promote and encourage Open Science: transparency, reproducibility, data provenance. E.g. Open Bioinforma5cs Founda5on hgp://open-‐bio.org • Public repositories make solware easily accessible and allow collabora5ve efforts, e.g. GitHub 24 hgps://github.com/
- 25. 25 Biocura5on 2015 Data Visualiza5on & Annota5on 8th Interna5onal Biocura5on Conference Our Panelists 1. Lorna Richardson: eMouseAtlas and Image Informa5cs 2. Justyna Szostak: Curated Causal Biological Network Models