Visual Analytics and
Biological Information
Chris Shaw
School of Interactive Arts & Technology,
Simon Fraser University
______________________________________________________________________________________
SCHOOL OF INTERACTIVE ARTS + TECHNOLOGY [SIAT] | WWW.SIAT.SFU.CA
Visual Analytics:
Integrated Interdisciplinary R&D
Information
Systems
Cognitive
Science
Visual
Analytics
Graphic &
Interaction
Design
Oct 8, 2009
Mathematical &
Statistical Methods
2
Interdisciplinary Know-how
•
SFU School of
Interactive Arts & Tech
– Design focus
– Technology and Science
– Cross-disciplinary Ph.D,
MSc. BSc.
•
UBC Media & Graphics
Interdisciplinary Centre
(MAGIC)
– 15 years use-inspired basic
research,Co-development
with industry & government
3
People
Oct 8, 2009
IMAS: Interactive Multigenomic
Analysis System
4
Visual Analytics
• Two broad task domains:
– Analysis of large datasets
• Overall interaction time: hours to weeks
• EG. the VAST Contest: Find the threat
represented in this large collection of text,
images, emails…
– Monitoring and emergency response
• Overall interaction time: seconds to minutes
• EG. Airport security screening: Find the
smuggled weapon
Oct 8, 2009
5
NSERC Strategic Grant:
“Visual Analytics for Safety & Security”
•
Application-aware basic research on humancomputer cognitive systems
–
Perceptual & spatial cognition stream
Sensemaking, levels/types of user expertise
New cognitive collaboration & coordination stream
–
Application development stream
–
–
•
Goal is to co-develop human & tech aspects
–
Understanding users, system customization and
training is as important as new technology
Oct 8, 2009
6
New Research Methods
• Conventional qualitative and
quantitative methods (e.g. grounded
theory, stats)
• Add advanced statistical,
computational and math models
• Integrated mixed-methods analysis
• Calls for visual analytics tools for visual
analytics research-- we are our testbed
Oct 8, 2009
7
Goal is integrated Visual Analytics R&D
improved
understanding
pure basic
research
existing
understanding
Oct 8, 2009
improved
technology
use-inspired
basic research
applied
research and
development
existing
technology
8
Science in the development process
Walkthrough or experiment
Design for key
human information
processing systems
Interaction
Science
Assess specific
aspects of
interaction
Implement prototype
Oct 8, 2009
9
Steps From Research to Practice
• VA science gives us design know-how
• VA-aware designers work with user
community to apply principles in design
• VA users trained to get maximum
advantage from VA
• VA-sophisticated organizations work
with designers to co-evolve new
technology and work practices
Oct 8, 2009
10
Biological Sequence Analysis
• Visual Analytics in the domain of
Biology
– Large data
– Non-spatial
– Many different layers of abstraction
Oct 8, 2009
11
Biological Sequence Analysis
• DNA Sequencing projects
• Visualization Systems
• IMAS
– Zoomable Sequence visualization
– Gene Finding
– BLAST Pairwise alignments
– Multialignments
• Results
Oct 8, 2009
12
DNA Sequencing Projects
• Similar sequence yields similar structure
• Similar structure yields similar function
Oct 8, 2009
13
IMAS supports
• Initial stages of analyzing DNA sequence:
– Find genes
– Find and Analyze similar genes
– Multialign like genes to find active sites
• Pipeline structure
Oct 8, 2009
14
Existing Tools
• Typically web-based
– Copy and paste sequence into text entry box
– Await search or analysis on remote database
– Get an isolated report that the user must
organize
• Visualization often done as a reporting function
– UCSC Genome browser, LLNL ECR browser,
NCBI annotation viewer
Oct 8, 2009
15
Desktop Workbenches
• Local sequence data
– Mix of local and remote analyses
• Web queries to remote data
– Bluejay, Apollo, Vector NTI, CLC Workbench
• User must work to integrate analyses
• Workbench is point of collection
Oct 8, 2009
16
IMAS
•
•
•
•
Integrates analysis and display
Horizontally Zoomable along sequence
Selectable detail vertical
Maintains a sequence analysis data
collection
• Visual display aligned to sequence
Oct 8, 2009
17
IMAS
Screenshot
Oct 8, 2009
18
IMAS
Contents
• DNA Sequence (Nucleotide, or NT Seq)
• GC % plot
• 3 forward & 3 Reverse complement
Amino Acid sequences:
Oct 8, 2009
19
Genes
• Built-in access to Glimmer 3.02 gene finder
• The labelled boxes are anchors for sequence
analysis
• Segments of DNA can also be marked as a
Feature for further analysis
Oct 8, 2009
20
Analyses
• Rricke104 gene has
– 2 NT BLAST pairs
– 1 AA BLAST pair
– 1 NT multialignment
– 1 AA multialignment
Oct 8, 2009
21
Pairwise Sequence Analysis
• Activated by selecting a Gene/Feature
and selecting NT or AA similarity search
• NCBI’s BLAST is called to search local
databases of NT or AA sequences
• Can also search NCBI central database
Oct 8, 2009
22
BLAST Alignments
• High Scoring Pairs are stacked from
most to least significant score
• Detail shown when zoomed in
• Pair similarity is shown using
background color
– Darker blue indicates higher similarity
• When zoomed out, text is hidden and
only similarity is shown
Oct 8, 2009
23
Multialignments
• Select BLAST alignments to be multialigned
• Clustal-W performs multialignment
• Aligns
– The originating IMAS gene sequence
– The “Full” sequence found by BLAST
• Not just the high-quality section
– Useful to align entire genes, or entire
corresponding segments of DNA
Originating Gene
BLAST Results
Oct 8, 2009
24
Oct 30,
8, 2009
2007
IMAS: Interactive Multigenomic Analysis System
25
Results
• Analyzed Orientia Tsutsugamushi
(Scrub Typhus)
– Found not much similarity in NT sequence
– Found a large number of SMART domains
not found in the related Rickettsia
organisms
• IMAS Benefit was data organization
Oct 8, 2009
26
Discussion
• Visualization Problems
– Pair alignments need better organization
• Local visibility and organization needed
– Overlap in X causes stacking layout problems
• Need selective relaxation of vertical alignment rule
Oct 8, 2009
27
Discussion
• Analysis Problems
– More flexible access to tools: Restriction
enzyme sites, methylation sites, Motifs,
Primers, Transcription regulation,
Intergenic signals......
• Database mediation problem: Please use XML!
– More flexible manipulation of sequence
parts
• Right now IMAS is somewhat rigid in its
worldview
Oct 8, 2009
28
Multiple Genomes
• Lots of organisms now sequenced:
– Learn from individual similarities
– Learn from similar gene organization
– Co-location “Synteny” of genes helps infer
similar function:
• Located together -> expressed together
Oct 8, 2009
29
Synteny Visualization
• Line up the similar organisms below
primary organism
– Draw links to connect them
– Take care to manage visual salience
Oct 8, 2009
30
Synteny Visualization Final
Oct 8, 2009
31
IMAS Synteny
• Not so good with reversals:
Oct 8, 2009
32
Alternative: Spring Synteny
• Orthologs as a node-link diagram
• 2 Link types
– Neighbors on same organism
– Sequence alignment (orthologous) links
Oct 8, 2009
33
Alignment Links
• Percent Identity Plot along sequence
• Framed to show PIP range
• RRickettsia linked to RConorii, RProwazekii, RTyphi,
RAkari
Oct 8, 2009
34
Springs
• Primary organism is central spine
– Secondary sequence have parallel track
connected by similarity links
– Each secondary sequence has its own
resting length for similarity links
• Length of neighbor links is blend of
– NT coordinate difference
– ln(length) * ln(length)
Oct 8, 2009
35
Neighbor links
• Using NT distance gives network
shapes with many acute angles
• Directly displays relative lengths of
genomes
Oct 8, 2009
36
Rrickettsii Genomes
Oct 8, 2009
Genomic Spring-Synteny
Visualization with IMAS
37
Results
– Advantages:
• Shows reversals clearly
• Shows gene “splits” with respect to primary genome
• Shows insertions/deletions
– Disadvantages
• Obscures length relationships
• Force-directed layout requires fiddling
• Rotating the similarity edge makes comparing
similarity difficult
Oct 30, 2007
38
Results
• Trade-off:
– Free 2 dimensions for gene placement
• Get to locate similar items close to each other
• Get ability to see gross rearrangements
• Lose ability to see detailed similarity along DNA
sequence
• Lose geometric location information
• Lose regulatory info (not represented)
Oct 8, 2009
39
IMAS
• Supports annotation pipeline
• Tree or DAG visualization, where
– Branches are individual BLAST runs
– Branches converge on multialignments
• Biologists want more!
– Analyze arbitrary collections of sequence
Oct 8, 2009
40
More
• Want ability to interactively cut, edit, and
analyze sequence
• “Genomic Spreadsheet” where
– Manage Sequences
– Compare & Align sequences
– Search for similar sequences
– Manage sequences at levels of abstraction
higher than sequence + annotation text
Oct 8, 2009
41
CzSaw
• A Visual Analytics System for Text Data
• Built by the SIAT CzSaw group
– Victor Chen, Dustin Dunsmuir, Nazanin Kadivar,
Eric Lee, Cheryl Qian
– John Dill, Chris Shaw, Rob Woodbury
Oct 8, 2009
42
Exploring Data
Analysis
Visualizing
Capturing Analysis
Analysis
Model
Data
Analysis Model
Process Process
Data
Analysis
History
CzSaw
Exploring Data
Capturing Analysis
Process
Visualizing
Analysis Model
Analysis
History
Conclusions
• Building IMAS helped us discover that
IMAS is not yet what you want
• Supports pipeline
• Need to analyze with respect to many
data types
– Genome & other ontologies
– Phylogeny
– Metabolic networks
– Regulatory networks
Oct 8, 2009
50
Thanks!
• Slides: Brian Fisher & John Dill
• Greg Dasch, Marina Eremeeva,
– Viral and Rickettsial Zoonoses Branch,
CDC Atlanta
Oct 8, 2009
51