MetaCore data analysis suite and functional analysis

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MetaCore data analysis suite and functional
analysis
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Knowledge-based functional data analysis
Cancer relevant annotations, datatabases, Active
cpds analysis screening
Data parsing, normalization
HTS, HCS
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Knowledge base Three interactions domains
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Pathways are assembled of interaction building
blocks
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Knowledge base gt100,000 biological pathways
Pic.1
Ligand Receptor Second messenger Enzyme
TF
Ligand TF TF target Reaction metabolit
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Ligand Receptor Adaptor Enzyme TF TF
target
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Distribution of interactions by mechanism
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Molecular functions in Database
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Compounds distribution by target
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Protein complexes/groups (species-specific)
Protein structure map (group, complex or group of
complexes) is shown on protein page
Chosen protein highlighted
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Disease maps. Delta 508 degradation in cystic
fibrosis
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Pathways Integration

• Interactive, static maps
• 400 maps
• Signaling, regulation, metabolism, diseases
• Backbone of formalized state of art in the field

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Concurrent visualization of different data types,
experiments
Agilent
Affymetrix
Proteomic
SAGE
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Types of biomarkers
Biomarker is a substance used as an indicator of
a biologic state Wikipedia
Biomarkers
Trust, utility

• Gene signature validation
• Large scale studies
• Statistical models
• Statistical clustering
• Need validation studies
• Long term and expensive
• Poor in cross-platform
• Poor robustness
• No mechanism

• Functional descriptors
• Large scale studies
• Statistical models
• Functional clustering
• Need validation studies
• Long term and expensive
• Validated cross-platform
• High robustness
• Mechanism

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Functional analysis must run in parallel to
statistical signatures. Why?
Differentially expressed genes, proteins
(normalization, QC)
Statistical analysis, Gene signatures

• Functional analysis
• Pathways,
• Networks
• Process ontology
• Disease ontology
• Toxicity ontology

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Gene signatures usually dont make functional
sense. No concise networks
70-genes metastases signature tVeer DI network
van 'T Veer L. J., Dai H, van de Vijer, M.J., ,
He Y.D., et al, Gene expression profiling
predicts clinical outcome of breast
cancer. Nature, 2002, 415, 530-36 Wang Y. et al.
Gene-expression profiles to predict distant
metastasis of
lymph-node-negative primary breast cancer.
Lancet, 2005, 365, 671-79
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76-genes metastases signature Wang DI network
Gene signatures usually dont make functional
sense. No concise networks
van 'T Veer L. J., Dai H, van de Vijer, M.J., ,
He Y.D., et al, Gene expression profiling
predicts clinical outcome of breast
cancer. Nature, 2002, 415, 530-36 Wang Y. et al.
Gene-expression profiles to predict distant
metastasis of
lymph-node-negative primary breast cancer.
Lancet, 2005, 365, 671-79
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Knowledge-based functional analysis

• Enrichment by category
• Signaling processes
• Metabolic processes
• Diseases
• Toxicities

• Networks
• Based on binary interactions
• Multiple algorithms
• Filers for specificity

Match IDs
Match IDs

• Parsed data
• -Gene IDs (LLs, GeneBank)
• Protein IDs (Uniprot)
• Compound IDs (CAS, smiles)

Match IDs

• Canonical pathways
• Signaling and metabolic pathways
• Linear, 12-13 steps
• vertical from MR to TF to effect

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Top canonical maps for two signatures for same
condition (metastases in invasive breast
cancers). Not comparable
Wang
Vteer
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Functional analysis tools
Resolution

• Experiment filters
• Species, orthologs, localizations, tissues etc.
• Custom list of targets, IDs

1000 genes Multiple sets

• Enrichment analysis for gene, protein, compound
  sets
• Hyper G, GSEA, GSA etc.
• Multidimensional analysis multiple ontologies
• GO processes
• GG processes
• Canonical pathways
• Diseases
• Export of sub-sets for network analysis
• Low resolution

• Interactome analysis
• Whole-set analysis
• Over- and underconnected nodes in the dataset
• Interactions neighborhood
• TFs, kinases, receptors, etc.
• Scoring for interactions within set FDR

• Network analysis
• Multiple pre-filters (species, interactions
  mechanisms,
• organelles etc.)
• Parameters enrichment with genes from set,
• canonical pathways, specific protein classes
• Algorithms SP, DI, AN, TFs, Receptors etc.
• Statistics hubs, preferred pathways etc.
• Highest resolution individual proteins or
  isoforms

Most important genes

• - Highly connected
• TFs, receptors, etc.
• Hubs from important networks
• Highest expressed/mutated genes

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Interactome analysis
A
B
P21 206 interactions in 16,000 proteins db
All proteins in the database
Amplicome
Mutome

• Calculates local and global connectivity
• for arbitrary datasets and lists
• Intra-connectivity within set
• Inter-connectivity
• Between sets
• Between set and all proteins in database

Under-connection
Over-connection
Expectation
P21 expect 5 interactions in 320 proteins
dataset
3 interactions for P21
9 interactions for P21
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Data workflow
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Interactome analysis
A
B
P21 206 interactions in 16,000 proteins db
All proteins in the database
Amplicome
Mutome

• Calculates local and global connectivity
• for arbitrary datasets and lists
• Intra-connectivity within set
• Inter-connectivity
• Between sets
• Between set and all proteins in database

Under-connection
Over-connection
Expectation
P21 expect 5 interactions in 320 proteins
dataset
3 interactions for P21
9 interactions for P21
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Transcription regulation between datasets
A
B
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Interconnectivity between mutome, amplicons
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Functional synergy in enrichment analysis
EA in ontology KEGG, GO, GeneGo pathways,
disease biomarkers etc..
Unconnected datasets
Pathway 1 p-value 4e-8 Pathway 2 1.2e-7 .
Synergistic datasets
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DM data parsers

• Custom interactions data
• Y2H
• Pull-down
• Co-expression
• annotation

Structures sdf, MOL
HTS, HCS
Custom maps, networks, pathways
Metabolites
Gene lists
Molecular bio data
ISIS DB
HTS, HCS
Pathway Editor
MapEditor
MetaLink
Structure parser
General parser
Metabolic parser
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Batch upload Browse files in
2. Click Next
1. Browse in file to upload. For batch upload,
choose zip archive files
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Experimental filters and options
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Results management

• Analysis results saved in different formats
• - Gene lists
• - Network object lists
• - Workflows
• - Network lists
• - Networks
• - Netshots
• - New maps
• Hierarchical folders structure
• One-button activation of experiments
• Graphics saved in up to 300dpi format
• (cover publication quality)

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Data export

• Experiments
• Gene lists
• Network object lists
• Excel or Word or text formats

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Choose a disease
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Cancer-related Diseases and Maps Distributions
by GO terms
Ontologies that contain specified targets will be
emphasized with green font
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Top 5 Maps will be saved to report
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Network are built and saved to report and to the
workflows folder in the Data Manager. Network
statistics are generated and reported for
common, similar, and unique genes.
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Included in MetaCore
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MetaSearch overview

• Flexible way to extract data from
  MetaCore/MetaDrug Discovery Platform
  (MetaDiscovery)
• Real-time communication with MetaDiscovery tools
• Results can be visualized and analyzed in
  MetaCore and MetaDrug

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24. The query is complete
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25. Click Go!, to launch MetaSearch
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26. As a result we obtain a list of 27 chemicals
(from over 600 000 chemicals from the database),
that satisfy our conditions Interact with
human beta 2 adrenoreceptor, activating it at
less than 100 nM concentrations
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MapEditor Lite/MetaMap
AFTER
BEFORE
Wet lab research, Writing reviews
Minutes Hours
Days? Weeks?
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MapEditor
Additional Localizations can be added
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Your NEW map is now an interactive part of
MetaCore
Users can visualize their experimental data on
the new map
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Direct export of interaction refs to EndNote