Transcript Presentation

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
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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
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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
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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
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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
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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
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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
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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
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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
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IMAS
Screenshot
Oct 8, 2009
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IMAS
Contents
• DNA Sequence (Nucleotide, or NT Seq)
• GC % plot
• 3 forward & 3 Reverse complement
Amino Acid sequences:
Oct 8, 2009
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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
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Analyses
• Rricke104 gene has
– 2 NT BLAST pairs
– 1 AA BLAST pair
– 1 NT multialignment
– 1 AA multialignment
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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
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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
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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
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Oct 30,
8, 2009
2007
IMAS: Interactive Multigenomic Analysis System
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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
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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
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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
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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
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Synteny Visualization
• Line up the similar organisms below
primary organism
– Draw links to connect them
– Take care to manage visual salience
Oct 8, 2009
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Synteny Visualization Final
Oct 8, 2009
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IMAS Synteny
• Not so good with reversals:
Oct 8, 2009
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Alternative: Spring Synteny
• Orthologs as a node-link diagram
• 2 Link types
– Neighbors on same organism
– Sequence alignment (orthologous) links
Oct 8, 2009
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Alignment Links
• Percent Identity Plot along sequence
• Framed to show PIP range
• RRickettsia linked to RConorii, RProwazekii, RTyphi,
RAkari
Oct 8, 2009
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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
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Neighbor links
• Using NT distance gives network
shapes with many acute angles
• Directly displays relative lengths of
genomes
Oct 8, 2009
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Rrickettsii Genomes
Oct 8, 2009
Genomic Spring-Synteny
Visualization with IMAS
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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
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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
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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
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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
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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
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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
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Thanks!
• Slides: Brian Fisher & John Dill
• Greg Dasch, Marina Eremeeva,
– Viral and Rickettsial Zoonoses Branch,
CDC Atlanta
Oct 8, 2009
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