Drawing, clustering and visualization of biological pathways.

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Drawing, clustering and visualization of
biological pathways.

• Fabien Jourdan,
• LIRMM, Montpellier France.
• fjourdan_at_lirmm.fr

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Visualization

• Enhances Data analysis.

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From data extraction to visualization
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From data extraction to visualization
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From data extraction to visualization
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From data extraction to visualization
Data Extraction
Visualization
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Visualization Process

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Visualization Process

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Visualization Process

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Visualization Process

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Visualization loop
Content
Browse
Model
Internal Model
Browsing Strategy
Formulate a Browsing Strategy
Interpret
Interpretation
Spence Diagram

• Visualization is not a linear process !

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Metabolic Pathway visualization
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Metabolic Pathway visualization
KEGG
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Metabolic Pathway visualization
EcoCyc MetaCyc
And many other tools
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Metabolic Pathway visualization
EcoCyc MetaCyc
And many other tools
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Visualization Loop

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Importing Data
DB2
DB3
DB4
DB1

• Information is merged in visualization not in
  databases
• Data is organized under an easy to use and to
  exchange format (e. g. XML)

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Importing Data
DB2
DB3
DB4
DB1

• Information is merged in visualization not in
  databases
• Data is organized under an easy to use and to
  exchange format (e. g. XML)

Query engine
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Importing Data
DB2
DB3
DB4
DB1

• Information is merged in visualization not in
  databases
• Data is organized under an easy to use and to
  exchange format (e. g. XML)

Query engine
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Importing Data

• Information is merged in visualization not in
  databases
• Data is organized using a standard exchange
  format (XML)

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KEGG pathways database
Map000100 C1 X10 Y30 C2 X5 Y2 C3 X45 Y99
C1E1-gtC2 C2E3-gtC3 C4E2-gtC3
Map000100 C1-gtC2 C2-gtC3
Map000100 C1 X10 Y30 C2 X5 Y2 C3 X45 Y99
Map000100 C1-gtC2 C2-gtC3
Map000100 C1 X10 Y30 C2 X5 Y2 C3 X45 Y99
Map000100 C1-gtC2 C2-gtC3
Map000100 C1 X10 Y30 C2 X5 Y2 C3 X45 Y99
Map000100 C1-gtC2 C2-gtC3
KGML an XML description for each metabolic
pathway
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Importing Data

• Information is merged in visualization not in
  databases
• Data is organized using a standard exchange
  format (XML)

KGML
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Main steps in visualization.

• Importing Data
• Finding relevant sources
• Organizing data according to future visualization
• Drawing
• Following drawing conventions or porposing new
  representations
• Providing drawing algorithm
• Linking Data and Drawing
• Assure that data could be access through the
  representation (drawing)
• Navigation
• Providing synthetical views of data (clustering)
• Enhancing data discovering through navigation

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Drawing

• Providing new representations
• Using deeply rooted drawing conventions in
  Metabolic Pathway representations

ViMac
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• Rojas et al. / EcoCyc

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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Drawing Algorithms

• Detect strongly connected components
• ? a DAG
• Draw the DAG with a DAG Placement algorithm
• Draw each component with Force Directed Placement

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• Rojas et al. / EcoCyc

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Drawing

• Providing new representations
• Using deeply rooted drawing conventions in
  Metabolic Pathway representations

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Drawing

• Providing new representations
• Using deeply rooted drawing conventions in
  Metabolic Pathway representations

KEGG
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Drawing

• Providing new representations
• Using deeply rooted drawing conventions in
  Metabolic Pathway representations

BIOTAG
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Interacting on metabolic pathwyas
BIOTAG
KEGG
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Drawing

• Our method
• Use KGML files
• The implicit data structure does not match the
  KEGG drawing of the network
• Data structure transformation
• Place elements according to KGML coordinates
• Compute edge routes

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Drawing

• Our method
• Use KGML files
• The implicit data structure does not match the
  KEGG drawing of the network
• Data structure transformation
• Place elements according to KGML coordinates
• Compute edge routes

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Drawing

• The network described in KGML is not the one we
  want to draw

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Drawing

• Our method
• Use KGML files
• The implicit data structure does not match the
  KEGG drawing of the network
• Data structure transformation
• Place elements according to KGML coordinates
• Compute edge routes

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Drawing Algorithms

• From KGML data our aim is to compute this
  representation

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Drawing Algorithms

• Graphical informations given in KGML files

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Drawing Algorithms

• Graphical informations given in KGML files

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Drawing Algorithms

• Compute barycenter of enzymes

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Drawing Algorithms

• According to the three defined coordinates route
  the edge.

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Drawing Algorithms

• According to the three defined coordinates route
  the edge.

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Drawing Algorithms

• From KGML data our aim is to compute this
  representation

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Drawing Algorithms

• Using KEGG coordinates provided in KGML files
• Routing Edges on a grid.

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Visualization Loop

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Linking Data and Drawing
DATA
Visualization
BIOTAG
User
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Linking Data and Drawing
DATA
Visualization
BIOTAG
User
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Visualization Loop

• Import Data
• Clearly separate data from representation
• Organize data according to future visualization
  in a separate process
• Drawing
• Follow drawing conventions or propose new
  representations
• Provide drawing algorithms
• Link Data and Drawing
• Make sure that data can be accessed through the
  representation (drawing)
• Navigation
• Provide direct access to data (multiple views)
• Provide synthetic views of data (clustering)
• Enhance data discovering through navigation

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Navigation Clustering
A. J. Enright PNAS 2002
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Small World Networks

• Short path between each pair of elements
• Each element neighbourhood is densely connected

• Metabolic pathways
• Protein-protein interaction networks
• Social networks
• Software component networks
• Hypermedia networks
• .

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Navigation Clustering

• Giving a synthetical view of data
• According to their values
• Acdording to their organisation (structure)
• Grouping elements
• Manualy
• Automaticaly

Multiscale Visualization of Small World
Networks InfoVis 03.
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Navigation Clustering

• Giving a synthetical view of data
• According to their values
• Acdording to their organisation (structure)
• Grouping elements
• Manualy
• Automaticaly

Multiscale Visualization of Small World
Networks InfoVis 03.
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Navigation Clustering
Software component capture using graph
clustering IWPC 03.
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Navigation Clustering

• Giving a synthetical view of data
• According to their values
• Acdording to their organisation (structure)
• Grouping elements
• Manualy
• Automaticaly

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Navigation keeping context

• When looking closer at an element, keeping the
  contextual information
• An overview frame
• A Fisheye Semantic Zooming

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Navigation keeping context

• When looking closer at an element, keeping the
  contextual information
• An overview frame
• A Fisheye Semantic Zooming

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Navigation keeping context

• When looking closer at an element, keeping the
  contextual information
• An overview frame
• A Fisheye Semantic Zooming

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Conclusion

• Visualization a tool to support data analysis
• Analysis of post-genomic data through metabolic
  pathway visualization (Biotag)
• Eploratory analysis (Protein-protein / Small
  World)
• Ongoing work
• Full implementation of fisheye techniques
• Validation of metric-based clustering

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Acknoledgements

• Transcriptome team
• Jacques Marti (Montpellier UM2)
• Oliver Clement (Montpellier UM2)
• David Piquemal (Montpellier UM2)
• Computer Science team
• Guy Melançon (Montpellier LIRMM)
• Isabelle Mougenot (Montpellier LIRMM)
• David Auber (Bordeaux Labri)
• Yves Chiricota (Chicoutimi UQAM)

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Thank you for your attention
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