2. Visualization in Science
The goal of visualization is to leverage existing
scientific methods by providing scientific insight
through visual methods. The National Science Foundation’s report
on “Visualization in Scientific Computing”, 1987
Visualization useful for:
- Acquiring new insights
- Gaining understanding of the problem
In molecular biology we have used different
methodologies to visualize proteins
17. What type of Visualization?
Information visualization vs. Scientific visualization
Information visualization is a process of transforming
data and information that are not inherently spatial
into a visual form, allowing the user to observe and
understand the information. (N. Gershon, foreword of the
proceedings of the first IEEE Symposium on Information Visualization)
Scientific visualization deals with data that usually
has an intrinsic representation. (JD. Fekete, Interactive
Information Visualization of a Million Items).
21. Does the difference between Information and
Scientific Visualization really matter?
• Visualization: (Scientific + Information) visualization
T.M. Rhyne
Does the difference between Information
and Scientific Visualization Really Matter?
• Bioinformatics data
• Visualization methods/techniques I.VS.V.
22. Visualization of Large Data Sets
• It is a problem of visualization, perception and
interaction
J.D. Fekete, Interactive Information Visualization of a Million Items, 2002
Visualization
• Visualization limited to a few thousand of items
• Techniques: aggregation, sampling and extracting,
occlusion and overlapping, grouping, …
23. Visualization of Large Data Sets
• Visualization should relay on preattentive graphical
features
• Features recognized “at glance” without effort
– Ex1: Finding a name in a sorted list
– Ex2: Spotting red dots among blue dots
Perception
24. Visualization of Large Data Sets
• Steps of visual information (“dynamic queries”):
– Overview of the data sets
– Zoom in on items of interest
– Filter out uninterested items
– Details on demand
Ahlberg and Shneiderman, Visual Information Seeking: Tight Coupling of
Dynamic Queries Filters with Satrfield Display, 1994
• Dealing with large data sets => brings interaction to a
very slow motion
Interactive techniques
26. Visualization of Protein Sequence Annotations
• Federate database systems => Large data sets
The Distributed Annotation System, 2001 Dowell et al;
BMC Bioinformatics. 2001; 2: 7. Published online 2001 October 10.
27. Visualization of Protein Sequence Annotations
• Distributed Annotation System.
• RESTful web service
• Uniform access to multiple repositories of biological
data.
• Repositories distributed in different geographical
locations.
• Different biological data types (Genome, Protein
sequence, Protein structure, …)
• System widely adopted.
• Requires visual clients (connect + merge + display).
• More ... http://biodas.org
DAS
31. Conclusion
• Researchers and Bioinformaticians are more used to
Scientific visualization of protein sequence
annotation.
• Information Visualization is a important additional
value to visualization of protein sequence annotation.
• Techniques, methods and ideas from both,
Information and Scientific Visualization, should be
taken into account for effective visualizations.
• We are just presenting a small percentage of protein
information. DAS is an example of how to connect
different data types. This brings new challenges in
protein visualization.
32. Thanks!
• To all of you for coming!
• And specially to …
– Nils Gehlenborg
– Henning Hermjakob
– Juan A. Vizcaino
Editor's Notes
Molecular hybridization
Protein expression profile
Similarity among experiments identifying proteins in plasma
Organized by metadata
Vector Space Model
S.V. & I.V. Overlaps and one can support each other
Ex1: Finding a name in a non-sorted list REQUIRES a time proportional to the number of labels
Ex2: When using more colors, the ERROR rate and time required to search colored items increase substantially