National Center for Biomedical Computing at

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• National Center for Biomedical Computing at
• Columbia University

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Mission

• Basic Science To study the organization of the
  complex networks of biochemical interactions
  whose concerted activity determines cellular
  processes at increasing levels of granularity.
• Software Tools To provide an integrative
  computational framework to organize molecular
  interactions in the cell into manageable context
  dependent components.
• Biomedical Applications To develop interoperable
  computational models and tools that can leverage
  such a map of cellular interactions to elucidate
  important biological processes and to address a
  variety of biomedical applications.

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MAGNet Organization
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Core I Computational Sciences
Coordinator Waltz Proj. Lead Leslie, Wiggins,
Friedman, Califano, Yemini Invest. Servedio,
Lussier, Kaiser, Ofran, Ross

• Machine Learning - Classification, Network
  analysis, Functional analysis.
• NLP - Analysis of Literature for biomedical
  content (genotytic/phenotypic)
• Software Design - BISON, an ontology for
  bioinformatics interoperability
• Biomedical Database Integration - GeneTegrate a
  semantic layer for bioinformatics data integration

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Core II Bioinformatics
Coordinator Rost Leaders Honig, Bussemaker,
Califano, Rzhetsky, Lussier Invest. Yemini,
Ofran, Petrey, Long, Anastassiou, Leslie,
Pavlidis, Wiggins, Friedman

• Protein Structure and Function - Sequence and
  structure based annotation of protein function
  (specifically protein-protein interactions)
• Reverse Engineering of Cellular Networks - An
  integrated knowledge-base of Cellular
  interactions in human B lymphocytes
• Cellular and Molecular Context - Using cellular
  and molecular phenotypes for context filtering
• MAGNet Tools - Software platform (geWorkbench)

Hot Topic
Hot Topic
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Core III Driving Biological Projects
Coordinator Califano Leaders Shapiro, Dalla
Favera, Gilliam Invest.

• Cell Adhesion - Structural and energetic basis of
  cadherin binding specificity A combined
  computational and experimental study
• Pathway Dysregulation - Regulatory Modules in
  Normal and Transformed B-Cells
• Complex Diseases - Genomic and Bioinformatics
  Solutions to the Search for Genetic Determinants
  of Common, Heritable Disorders Alzheimers
  Disease and Autism.

Hot Topic
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Core IV Infrastructure (cont.)
MAGNet/C2B2
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Hot Topic B-Cell Knowledge Base
Basic Science

• Everything you always wanted to know about B
  Cells but were afraid to ask

H. Bussemaker A. Califano R. Dalla Favera C.
Leslie A. Rzhetsky C. Wiggins
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Knowledge Base for Human B Lymphocytes

• Integrative
• Bayesian Evidence integration of pairwise
  interactions
• Protein-Protein, Protein-DNA
• Context Specific
• ARACNE, GeneWays, REDUCE
• B-Cell data or B-cell specific criteria
• Linked to one of the largest B-Cell expression
  profiles microarray dataset, ChIP-Chip assays
  (MYC/BCL6), miRNA profiles, and Literature
• Captures Multi-variate dependencies
• Three-way interactions via MINDY and MATRIXReduce
• Post-translational modulation of transcriptional
  regulation
• Combinatorial transcriptional regulation
• Signal transduction control of Transcriptional
  Regulation I.e. the Transferome meets the
  Transcriptome
• Links to literature, via GeneWays

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Knowledge Base for Human B Lymphocytes V1.0

• Integrative
• Bayesian Evidence integration of pairwise
  interactions
• Protein-Protein, Protein-DNA
• Context Specific
• ARACNE, GeneWays, REDUCE
• B-Cell data or B-cell specific criteria
• Linked to one of the largest B-Cell expression
  profiles microarray dataset, ChIP-Chip assays
  (MYC/BCL6), miRNA profiles, and Literature
• Captures Multi-variate dependencies
• Three-way interactions via MINDY and MATRIXReduce
• Post-translational modulation of transcriptional
  regulation
• Combinatorial transcriptional regulation
• Signal transduction control of Transcriptional
  Regulation I.e. the Transferome meets the
  Transcriptome
• Links to literature, via GeneWays

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Integrating protein-DNA and protein-protein
Interactions via Naïve Bayes Classification

• Protein-Protein Interactions (PPIs)
• Human PPI databases
• Human Protein Reference Database (HPRD)
• Biomolecular Interaction Network Database (BIND)
• Database of Interacting Proteins (DIP and IntAct)
• Y2H Studies (2in human)
• Eukaryotic PPI via hortologous genes
  (Inparanoid)
• MIPS, BIND, IntAct.
• GeneWays Predictions (context-specific literature
  analysis)
• Co-expression analysis (Mutual Information)
• Gene Ontology classification (biological
  process/compartment)
• Protein-DNA Interactions (PDIs)
• Human PDI databases
• TRANSFAC, BIND, MycDB
• Mouse PDI databases (TRANSFAC, BIND via
  orthologous genes (Inparanoid)
• ARACNE (bootstrap-TF)
• GeneWays predictions (context-specific literature
  analysis)

• 49,719 interactions (4,944 genes)
• 27,705 PPIs (4,209 genes)
• 22,014 PDIs (3,216 genes/457 TFs)

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Network Motifs
Protein complexes
Regulatory Motifs
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Definition of a Modulator

• Modulator genes capable of modulating the
  activity of transcription factors at
  post-transcriptional levels, i.e. without
  affecting its mRNA concentration (e.g. activating
  Kinase, co-factor, etc.)

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Algorithm Workflow

• Statistical Tests
• The gene gm is a modulator of the Interaction
  gTF ? gt if
• A modulator has sufficient expression range
• Modulators need to be statistically
  independent of the TF
• i.e., it does not condition the TF expression
  range
• Conditional MI difference is statistically
  different from zero

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An Example JUN as Cofactor of MYC
DNA Binding sites -2kb,2kb
Mutual Information
Candidate Targets
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MYC Modulation by Transferases
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MYC Modulation by co-Transcription Factors
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Hot Topic Caherins
Biomedical Applications

• Elucidating Cadherins binding specificity

L. Shapiro B. Honig
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Differential expression of cadherins is critical
for vertebrate development
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Cadherins structure
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Electron tomography reconstructions of
desmosomes.
He et al., Science, 302, 109-113, 2003.
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Asparagine Arginine
Serine Glutamine
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(No Transcript)
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(No Transcript)
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8NgtS, 23RgtQ N-Cadherin
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Homophilic adhesion is retained in the N-8/23
mutant
N-cadherin
N- 8/23 mutant
E-cadherin
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But adhesive specificity is converted to that of
E-cadherin
N-cad
E-cad
N- 8/23
E-cad
N-8/23
N-cad
4X
10X
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Hot Topic geWorkbench
MAGNet Software Tools

• An interoperable platform for integrative
  genomics research

Columbia University A. Califano A.
Floratos The BROAD Institute (GenePattern)
Jill Mesirov Michael Reich
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geWorkbench (genomic Workbench)

• Based on caWorkbench, an NCI/caBIG-funded effort
• Open source, Java based platform

• Integrated Genomics Platform
• Support for gene expression data, sequences,
  pathways, structure, etc. (40 visualization and
  analysis modules).
• Access to local and remote data sources and
  analytical services.
• Support for workflow scripting.
• Integration with caGRID.

• Development framework
• Open source development.
• Modular/extensible architecture, supporting
  pluggable components with configurable user
  interface.
• Formal (caBIG-registered) data models for
  multitude of bioinformatics concepts.
• Easy integration of 3rd party components.

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geWorkbench

• Major effort on making the platform broadly
  available to and extensible by the biomedical
  research community
• Main Vehicle for Integration and Dissemination of
  MAGNet Tools
• B cell Interaction Knowledge base
• ARACNE
• REDUCE
• MEDUSA
• GeneWays
• Protein Structure Pipeline
• JMol (Open source Molecular Viewer)
• Functional Annotation Pipeline
• Etc.

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Integration of 3rd party components
Cytoscape
GenePattern
MatrixREDUCE
GoMiner
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BISON Biomedical Informatics Structured Ontology
Component A
_at_Publish public DSDataSet publish(. . .)
DSDataSet dataSet // do some work that
assigns a value to dataSet. return
dataSet _at_Subscribe public void
receive(DSDataSet dataSet, Object source)
// Consume the argument dataSet, as
appropriate
Component B

• Provide re-usable models of common bioinformatics
  concepts
• Data sequence, expression, genotype, structure,
  proteomics
• Complex data structures patterns, clusters,
  HMMs, PSSMs, alignments
• Algorithms Clustering, matching, discovery,
  normalization, filtering
• Provide a foundation for the development of
  interoperable geWorkbench components
• Endorsed by multiple communities (caBIG, AMDeC,
  NCBCs)

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geWorkbench Resources
http//www.geworkbench.org/
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The DREAM Project
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Web Resources

• Center Overview
• magnet.c2b2.columbia.edu
• geWorkbench
• www.geworkbench.org
• DREAM Project
• www.nyas.org/ebriefreps/splash.asp?intEbriefID534