metacore/metadrug portal as an enterprise systems …...present a structure as a group of proteins...

Copyright GeneGo 2000-2007

Cover Slide

MetaCore/MetaDrug portal as an enterprise systems solution for multi-site distributed OMICs research


Agenda and Evaluation criteria

• Introductions• Introduction to GeneGo and GeneGo products

– Profitable, growing company, privately owned, no VC– Strong science

• Science and Cancer cell papers• $10MM in grants• Research services

• Ability to manipulate data• Data quality/content• Data Management

– Data input: batch uploading, automating through API, from third party tools and data manager

• Data export– Output: Excel, third party, EndNote, 300 dpi images, easy sharing

• Ability to influence future developments and integration of their pipelines, content and workflows

• On line help and tutorials


• Founded in 2000 by Dr Tatiana Nikolskaya

• 100+ employees and in hiring mode

• Products

– MetaCore™, MetaRodent™, MetaBase™, MapEditor™, MetaLink™

– MetaDrug™, MetaKinase™

– 1-2-3 Workflow™

– iPath

• Consortiums

– MetaTox™ Consortium (FDA, Vertex, Elan)

– MetaMiner™ Consortiums; Oncology, Metabolic, Cardiac, Immunology and CNS diseases

• Offices, expanding San Diego office

– St Joseph, San Diego, San Francisco, London, Moscow, Taipei, Tokyo

• Current grants and awards– 2005 $100K NIEHS grant SBIR Phase I (toxicogenomics)– 2005 $100K NIH grant SBIR Phase I (biomarkers)– 2005 subcontract for University of Michigan grant, Gil Ommen (proteomics)– 2005-06 $750K NIGMS SBIR Phase II (systems pharmacology)– 2006-07 $750K DOD/DARPA SBIR Phase II (proteomics)– 2006-07 $1 million NCI SBIR Phase II (breast cancer)– 2007 $1 million Toxicogenomics Phase II

• Publications– GeneGo peer-review publications

• Cancer Cell Paper on BC stem cells• Science, Johns Hopkins• FDA paper Nature Biotechnology

– Customer publications, presentations and postersCopyright GeneGo 2000-2005

GeneGo Overview


GeneGo Academic & Regulatory Customers

“The manually curated content available from GeneGo is exceptional and will allow us to advance our projects,” said Dr Nat Goodman, a Senior Research Scientist at ISB. “We are initiating several research projects together that will hopefully result in publications.”


Collaborations

Genespring

http://www.spotfire.com/

http://www.illumina.com/home.ilmn

http://www.expressionanalysis.com/

http://www.goldenhelix.com/

http://www.invitrogen.com/

http://www.massspec.com/

http://www.accelrys.com/


GeneGo Environment

Enterprise solution

Relational Database45 PhD’s and 5 M.D.’s

Databases

GVKPubmedOMIM

UnigeneABI Body Atlas

Swiss ProtGeneProtInternal

VisualizationMaps

Networks

ContentInteraction

GenesProteins

MetabolitesXenobioticsPathwaysOntologies

ToolsNetworksPathways

EnrichmentData

depositoryData

comparisonPrioritization

ExperimentData

Gene expressionProteomics

MetabolomicsSNPsSAGESiRNA

CHiP ChipHCS

StatisticsAlgorithms Custom interactions

North America-Europe-Asia

Research-Development-Clinical TrialsWeb Client

Server


Ligands: metabolites, peptides, xenoboitics

Membrane receptors

Signal transduction:G proteins,

Secondary messengersKinases

Phosphotases

Transcription factors

Core effect: metabolic pathways

Metabolites

•6,600 drugs w/targets• 4,000 endogenous metabolites•>20,000 ligand-receptor interactions•850 GPCRs and other membrane receptors• Nuclear hormone receptors

Three interactions domains in MetaCore

>147K manually curated physical signaling interactions550 canonical maps

43,000 12-step canonical pathways

900 Human transcription factors4100 target genes

11,000 metabolic reactions

110 Fine metabolic maps-Unique!

4,000 endogenous metabolites


Unique: Interactions from different domains on merged networks

Metabolic reactions Endogenous metabolites

Genes and proteins


Networks of protein interactions

– Dynamic and interactive

– Exploratory tool

– Build new pathways for genes of interest

Pathway Integration

Interactive, static maps

– 550 proprietary maps

• With thousands of canonical pathays

– Signaling, regulation, metabolism, diseases

– Backbone of formalized “state of art” in the field


Canonical pathways


Unique multi step canonical interactions on networks

List of pathways (cascades from canonical maps) that

occur on network

Selected pathways are highlighted


MetaRodentMetaRodent

MetaRodent

New add on module

The first rat and mouse specific pathway analysis platform

Genes and proteins specific to rat and mouseProtein complexes specific to rat and mouseSpecies specific canonical pathway maps


Export formExport form

Species added to export form


Androsteneone and testosterone biosynthesis and metabolism (part1)

Enzyme AST: gene Spm2A (rat)

Enzyme HSD3B5 – gene Hsd3b5 (mouse)

Enzyme SULT2A2

Enzyme HSD3B4

Sulfotransferase

Steroid dehydrogenase


Systems ReconstructionTM Technology

Ontologies in MetaMiner

Ontologies from GeneGo


Entities, attributes and relationships

Disease tree

Disease-tree branch

Disease page Disease description

List of genes, RNAs, and proteins associated with disease including

protein biomarkers list

Chemical biomarkers list

Drugs list

Gene page

Chem. page

Gene-disease link information

Disease pathway NWs

Disease pathway maps

Correspondent protein NW objects

CorrespondingProtein page

RNA page

Complex or groupProtein page

RNA-disease link information

Protein-disease link information

Chem-disease link information

Drug action pathway maps

Correspondent chem. NW objects

NW objects interaction

Disease-specific interactions list

Disease-specific interactioninformation


Gene, RNA, protein, compound – disease relationships in ontology

DNA Possible difect typemodification (methylation, for example)mutationSNPrearrangement amplificationlocus change

RNAalternative transcriptsplice-variant

Proteinposttranslation modification splice-variant isoformamino acid exchangealteration of interactionalteration of localization

influence on protein activity

application (biomarker)

influence on protein activityalteration quantity of RNA

application (biomarker)

alteration of protein activityalteration of protein quantity

application (biomarker)application (drug target)

Compound

alteration of compound quantity

application (biomarker)application (drug)

Causative relationscause/predisposition

risk

hypothesis manifistation protectionno relation


Disease Tree Structure. Example of ambiguity in MeSH


Cardiac Arrhythmia ontology in MetaMiner


Cardiac Arrhythmia: summary of gene-disease relationships 60 genes (auto)

23 genes

5 genes

22 genes7 genes

23 approve drugs (curated)

82 genes (curated)

147 inducing compounds (auto)


Drugs in ontology: relationships for indication

Coronary Arteriosclerosis

Hypertension

EdemaGlaucoma

Seizures

Heart Failure

Angina

Epilepsy

Ovarian/Lung neoplasm

• Drug target

• Approved drugs

Transport H+ and CO(2)


Metabolic pathway

Cholestasis

Hepatotoxicity

NephrotoxicityTubular toxicity

Cardiomyopathy

Anemia

Ototoxicity

Drugs in ontology: relationships for toxicity


Entity

OWL schema for disease-centered ontology in MetaMiner


Gene-Disease connections in public domain and GeneGo

GENE MeSH

•Hierarchical structuredisease classification 4,888 diseases•Genes associated with diseases 6,429•Cited articles 33, 792

Public domain does not have structured information about disease connectivity(by clinical classification) and causative relations with genes and proteins

Only citation with Diseases name. Low trust

Only hierarchical structure

disease tree

OMIM

Only genetic info (mutation, SNPs)-No expression- No protein activity, loc

GeneGo


Drug toxicity tree

Folders from MeSH GeneGo tox ontologies based on reviews

38 Drug-induced pathological processes


MetaSearch™MetaSearch™Included in MetaCore


Creating experiment based on search resultsCreating experiment based on search results

List of genes searched for are exported to MetaCore™ as an

“experiment”


New interactions modules

• MapEditor: custom maps– Draw pathways maps from scratch– Transform gene lists into networks into pathway maps– Edit MetaCore’s canonical maps– View and score your maps within the context of canonical maps– Map experimental data on custom maps

• MetaLink: overlaying custom interactions– Import custom interactions (Y2H, co-expression, pull-down, etc.)– Visualize using GeneGo network building algorithms– Score “unknown” proteins (high IP potential) based on relevance to

“benchmark” networks built from MetaCore interactions

• PathwayEditor: annotation technology transfer, at the database level– Custom annotation of interactions, compounds, diseases, metabolism in the

framework of internal annotation system at GeneGo– Use the annotation forms, workflows and QC system developed at GeneGo– Novel objects are imported and integrated with pre-existing data in MetaCore


What is MapEditor?

BEFORE AFTER

Days?Weeks?

MinutesHours

Wet lab research,Writing reviews

MAC or PC


MapEditor:Adding Interactions

Click on the source and target objects to create an interaction

If an interaction is already present in the database between the

objects, it will be listed here

Use the Link tool to create interactions between objects


MapEDitor:Edit and publish the new map


MapEditor:The new map is now a functional part of MetaCore


Resulting Direct Interactions network

Pink interactions are from the uploaded links file Mouse over an

interaction to see the uploaded weight value

Blue interactions are in both the links file and the MetaCore database

MetaLink: Mapping interaction sets on networks


MetaDrug™A unique systems pharmacology

platformGeneGo, Inc.

2007


Do in silico tools REALLY help in pre-clinical discovery?

Novel compound

Wet

lab

:- C

hem

istr

y- B

iolo

gy

IND applicationNeed BIOLOGAL effects:-Indication-Toxicity-Off target effects

Structure-basedmodeling

QSARs modelsactivity

Dry lab predictions

Pharmacophores

QSAR models metabolism

QSARs modelstoxicity

2-6

yea

rs;

10

-30

m?

Clinical trials: 9 out of 10 compounds fail

Is there any other way?

Why?


A NEW paradigm!

=1 protein at a time

•3,400 disease related genes•500+ diseases w/genes•600 known drug targets•3,000 toxicity related genes•700 cellular processes• 200,000 protein interactions

Present a structureas a GROUP of proteins

Query KNOWLEDGE base:- Diseases- Toxicities- Pathways- Processes- Networks

-Score based on analysis-Indication-Toxicity

-Select best structures

=Systems level analysis

Many, many products MetaDrug

IND BIOLOGAL effects:-Indication-Toxicity-Off target effects:

=

GROUPS OF PROTEINS!

Novel compounds

Structure-basedmodeling

QSARs modelsactivity

Dry lab predictions

Pharmacophores

QSAR models metabolism

QSARs modelstoxicity Report BY and FOR

end user = chemist

= Systems level effects


MetaDrug: applications

Hit-to-lead libraries

Lead optimization

Selection for IND

Libraries

Screening

Hits MetaDrug“Front load”

risk

1:12 makes it to market*Source:” Merck's Recall of Rofecoxib — A Strategic Perspective”. New England Journal of Medicine Volume 351:2147-2149

http://www.spotfire.com/


How do we do it?

Compound

Metabolites

Db similarity Db similarity

Lists of gene and protein IDs, some w/numerical data

Primary targets/first indication:-Diseases-GeneGo processes-GO processes-Drug target networks

Secondary targets/additional indications- Diseases- GeneGo processes- GO processes

- Drug target networks Secondary targets/off-target side effects

-Diseases-Toxicity networks- GeneGo processes-GO processes

Primary targets/human toxicity:-Toxicity networks-GeneGo processes-GO processes

Summary efficacy/tox index per indication


MetaDrug Applications

• Front load risks• Ranking of lead compounds

– Prioritizing hits• Compare internal compounds with drugs on the market• Use knowledge base to predict metabolites for screening

purposes• HTS• In silico tools speed up virtual screening for medicinal

chemists and DMPK groups• Identifying compound targets in synthetic lethal strains• Prediction of side effects• Drug-drug interaction• Identification of targets of natural products, separated from

bacteria and plants• Identification of exact molecular targets of molecules,

known to be active on a whole pathway with exact mechanism of action

• Comparing therapeutic drugs to endogenous compounds


Integration with MDL DiscoveryGate


Search compound of interest or its metabolites in MDL databases *(requires access to Discovery Gate)


Cover Slide

Data Management & Storage


OMICs Data Management system

Data IN (Parsing)

Data warehousing-Experiments-Gene/protein/compound lists- Maps

Results out (Export):-“Common language” format: Word, Excel- Compatibility with 3rd parties - Compliance with regulations: FDA-End Note for publications

- Reporting-Access/security

HTS, HCS

Results warehousing- Workflows- Network lists- Networks- Maps- Gene/protein/compounds lists

IDs converter

Data processing in MC/MD


Data Manager data parsers

HTS, HCS

Pathway Editor MapEditorMetaLink

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

Custom maps,networks, pathways

Structuressdf, MOLMolecular bio data HTS, HCSMetabolites ISIS DB

General parser Metabolic parser Structure parser

Gene lists


Data format conversion

DM

IN OUT

Human, mouse or rat out

Genomics


Unique:Functional analysis steps

• 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

1000 genes; Multiple sets

2-100 genes perentity

Top 5, 10 genesIn each class

Arbitrary # ofindividual proteins

and interactions

• 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

• Interactome analysis– Whole-set analysis– Over- and underconnected nodes in the dataset

• Interactions neighborhood• TFs, kinases, receptors, etc.

– Scoring for interactions within set: FDR

Resolution


GeneGo’s Data Manager as a repository and analysis suite

All data are stored and analyzed in MetaCore

We confirmed that these gene groups work on independent set of patients. 295 set was done on DIFFERENT chip, no norms, but still those groups of genes worked! We also were able to find MORE genes for each group of genes and we included them into this picture. On this larger set we were able to see small group of ERBB+/ESR- patients, which we didn’t see on 122 set (probably the set was too small and such patients are rare). We noticed that 4 new genes from ERS1 group: ANDR, DNALI1, XBP1, FOXA1 are always over-expressed in both ESR+ and ERBB+, so we decided to form another group of genes from them. For the same reason 5 upper genes from PLAU group (red):COL11A1; PLAU, PLAUR, WNT2, MMP14 were united into separate group, leaving COL5A2 out. Then we did re-clustering with 9 resulting groups of genes

Identified on 122 set:2. ERBB2

(STARD3,GRB7,ERBB2,THRAP4,PPARBP)3. ESR1 (GATA3, XBP1,TFF3,ACADSB,ESR1)4. Keratin 5/17 (KRT17, TRIM29,KRT5,GABRP)5. Keratin 8/18 (KRT18, KRT8, PP2CA,KRT8L2)6. PLAU (COL5A2, FAP, THY1, PLAU)7. STAT1 (STAT1, UBE2L6,TAP1,LAP3)8. CEACAM (CEACAM6, CEACAM5, CEACAM7)

Identified on 295 set:2. ERBB2 (AMPL,GRB7,ERBB2,THRAP4,PPARBP, LENEP)3. ESR1 (GATA3, XBP1,TFF3,ACADSB,ESR1)4. AR (AR, FOXA1, DNALI1)5. Keratin 5/17 (KRT17, TRIM29,KRT5,GABRP, COL11A1)6. Keratin 8/18 (KRT18, KRT8, PP2CA,KRT8L2)7. PLAU (MMP14, PLAU, PLAUR, WNT2)8. FN1 (FN1, THY1, COL5A2, BAPX1)9. STAT1 (STAT1, UBE2L6,TAP1,LAP3, CXCL10, PSMB9)10. CEACAM (CEACAM6, CEACAM5, CEACAM7, CEACAM3)

Sørlie 122

Sørlie 295


Export to EndNote

1. 2.

3.


Easy Data Sharing

Right click to share maps, networks and experiments


MetaSearch™ overview included in MetaCoreMetaSearch™ overview included in MetaCore

• Powerful, easy to use search tool– Flexible way to extract data from MetaCore/MetaDrug

Discovery Platform (MetaDiscovery)

• Customizable Boolean queries

• Real-time communication with MetaDiscovery™ tools

• Results can be visualized and analyzed in MetaCore™ and MetaDrug™


MetaSearch: starting your queryMetaSearch: starting your query

Start your query by selecting type of entities you would like to find. In

this example maps from MetaCore™


Step2: adding search termsStep2: adding search terms

We are looking for maps containing JAK1


Finding all maps with JAK1 Finding all maps with JAK1 ANDAND STAT1 STAT1

In the complete query we specify two criteria: maps should contain both JAK1

AND STAT1


Building nested queriesBuilding nested queries

We further restrict the search to maps NOT containing Oncostatin M


Expanding your queryExpanding your query

Then we extract all genes from these maps


Updates, Maintenance and Support

• Support– By phone– By email– On line and on site discussions

• Training– Free on line training

• Every first Monday of the month MetaCore, MetaRodent and MapEditor• Every last Monday of the month MetaDrug and Tox

– On site training• Training manuals

– MetaCore 4.5 Basic – MetaCore 4.5 Advanced– MetaBase– MapEditor– MetaDrug

• Tutorials, FAQ, new release information– Under Q&A tab

• Integrations documentation– Under integration tab


Cover SlideContact: Mark HughesTel: + 44 7786 150699Email: [email protected]

metacore/metadrug portal as an enterprise systems …...present a structure as a group of proteins...

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