# Presentation: 700516

## caArray Database in the cancerBiomedical Informatics Grid, caBIG

- Project Goals:
- MIAME compliant data annotation
- Data sharing
- Provide microarray data for analytical applications via the MAGE-OM API
- Data Integration across different domains in caBIG/caGRID

## The NCI Center for Bioinformatics:Who are we?

- The Center for Bioinformatics is the NCI’s strategic and tactical arm for research information management
- We collaborate with both intramural and extramural groups
- Production, service-oriented organization
- Mission to integrate and harmonize disparate research data

## Motivation for the caBIG program

-   Overwhelming volume of data
-   Multitude of sources
-   Each part of the health community speaks its own scientific    “dialect” (e.g. lab values,  genetic profile, clinical data)
-   Lack of consensus on common standards and terms
-   Lack of coordination across, and collaboration within, the cancer   research enterprise

**Notes:**
Reaching our goal of harnessing all the information that’s needed will not be easy. So, what are the challenges? 

First, at the risk of seeming insensitive to the tragedy a year ago, I can’t help comparing the challenge to an information tsunami—especially when you consider the huge amounts of data now being generated by genomics that have to be integrated. 

## Cancer Biomedical Informatics GridTM (caBIGTM)

- Common, widely distributed infrastructure permits research community to focus on innovation
- Shared vocabulary, data elements, data models facilitate information exchange
- Collection of interoperable applications developed to common standards
- Biomedical research data will be available for mining and integration

**Notes:**
This is the heart of what caBIG is setting out to do.  Essentially it’s an effort to bring different communities together through IT.  Widely distributed infrastructure means the community can share common infrastructure, so each individual institution can focus on its priority innovations.  Picture a common infrastructure that speaks common dialect with applications that sit on top so that people can ‘consume of the info’  with ultimate goal of ‘hot and cold running information’ available. 

## Current caBIGTM community

**NCI-designated Cancer Centers (50)**
**Academic Centers (integrated into broader biomedical infrastructure)**
**Stand-alone (community leaders)**
**Community outreach**
**NCI Divisions **
**NIH NECTAR grantees **
**Government**
**Industry **
**International Groups **
**Standards development organizations**
**U.K.’s National Cancer Research Institute**
**~800 active participants**

**Notes:**

But from the early days, we had many ‘volunteers’ from other cancer organizations, standards development organizations, We are working in a “FEDERATION” of academic, public, and private participants. Participating NCI Divisions include CCR, DCP, DCCPS..... [other] Participating NECTAR grantees include the University of Minnesota, University of Chicago, and Duke University.  NIH:   NINDS, NHLBI NIDCD are actively working to build infrastructure and apps that support their missions as well as ours. FDA is a very close collaborator on several projects related to regulatory reporting.  Industry.  Big pharma (Pfizer, Astra-zenica; IBM playing actively;  Oracle, SAIC, BAH.  Smaller biotech e.g. Velos, Percipenz—broad range of people engaged.  Strategic partnership with NCRI (NCI in UK) recently launched effort that will mirror caBIG.  NCRI will build components to enhance infrastructure and components

You can become a funded caBIG member. You need to understand that there are NO grants associated with caBIG. This is one of the BIGGEST differences about it, and it’s hard for many people to understand. This requires the signing of a contract spelling out a number of obligations including that all development be open source, which is development that is open for anyone to consume at no cost. Everything being done within caBIG is being done with contracts. You actually have to meet specific milestones and delivery dates for payment for which the activities are closely monitored. Funded members participate in the workspaces and special interest groups, working to make existing tools caBIG compatible, develop new tools or practices and putting these new caBIG tools and capabilities in place within their organizations. 

Or, you can become a caBIG partner. This involves the exchange of resources and typically involves an MOU but it is an unfunded activity. Typically vendors and other government agencies are caBIG partners.

Or, you can simply join the calls to seek information, give domain expertise and to actively participate in work that is changing the way we do research. Many start out this way in an effort to get their feet wet and to better understand where they and their organization would fit.

caBIG serves the community as a whole, and the community wants real problems to be solved.  

## Four Domain Workspaces and two Cross Cutting Workspaces have been launched

**DOMAIN WORKSPACE 3**
**Tissue Banks & Pathology Tools**

- Provides for the integration, development, and implementation of tissue and pathology tools.

**DOMAIN WORKSPACE 2**
**Integrative Cancer Research**

- Provides tools and systems to enable integration and sharing of information.

**DOMAIN WORKSPACE 1**
**Clinical Trial Management Systems**

- Addresses the need for consistent, open and comprehensive tools for clinical trials  management.

**CROSS CUTTING WORKSPACE 2**
**Architecture**

- Developing architectural standards and architecture necessary for other workspaces.

**CROSS CUTTING WORKSPACE 1**
**Vocabularies & Common ****Data Elements **

- Responsible for evaluating, developing, and integrating systems for vocabulary and ontology content, standards, and software systems for content delivery

**DOMAIN WORKSPACE 4**
**Imaging**

- Provides for the sharing and analysis of in vivo imaging data.

## caArray Data Portal 

- caArray Data Portal

- Number of raw data files in the database: 699 (565 .cel, 134 .gpr)
- Number of public experiments in the database: 35
- Registered users:306
- Number of downloads from the download center: 460
- Average unique users per month: 282
- Average time spent per session: 16 minutes

- http://carraydb.nci.nih.gov

## caArray: Compliance with Standardization Efforts

**MIAME**
- Minimum Information About a Microarray Experiment
- 1.1 Draft 6 (April 1, 2002)
- _[http://www.mged.org/Workgroups/MIAME/miame_1.1.html](http://www.mged.org/Workgroups/MIAME/miame_1.1.html)_
**MAGE-ML**
- MicroArray and GeneExpression Object Model and Markup Language
- 1.1 (October 2003)
- _[http://www.omg.org/docs/formal/03-10-01.pdf](http://www.omg.org/docs/formal/03-10-01.pdf)_
**MGED Ontology**
- Microarray Gene Expression Data Ontology
- 1.1.8 (April 2004)
- _[http://](http://mged.sourceforge.net/ontologies/MGEDontology.php)__[mged.sourceforge.net/ontologies/MGEDontology.php](http://mged.sourceforge.net/ontologies/MGEDontology.php)_

- caAMEL- caArray
- Integration
- October 2006

## caBIG Compatibility Guidelines

**Notes:**
I don’t want to get into the details now.  I do urge you to attend one of the Compatibility’Interoperability sessions coming up. 

[hold in reserve]
The caBIG compatibility guidelines describe the requirements for creating a system that will interoperate on the Grid. They cover 4 areas, interface integration (i.e. how the system is accessed), vocabularies/terminologies and ontologies, data elements and information models. 

## caArray Workflow

**caArray Workflow**

## MGED Ontology (MO) and caArray

- _Now_: MO stored in caArray_Future_: caArray integration with the Enterprise vocabularyservices (EVS)_Two Categories_:	1) Terms provided by MO, e.g. BioMaterialLabel	2) MO references existing ontologies, e.g. DiseaseState

- Mediator

**LexBIG**
**API**

**EVS**

**caCORE Server**

**caCORE**
**API**

**caArray**

## LabelCompound:

**LabelCompound:**
**Compounds that are used for labeling extracts.**

## Slide 13

## 
CellLineCellTypeClinicalHistoryClinicalTreatmentCompoundDevelopmentalStageDiseaseStagingDiseaseStateFamilyMemberGeographicLocationHistologyOrganismOrganismPartPhenotypeSequenceOntologyBioSequenceTypeStrainOrLineTargetedCellTypeTestTypeTumorGrading 

- or ontology that can be referred such as ICD-9 or
- Gene Ontology.
- Used in classes:

- CellLineCellTypeClinicalHistoryClinicalTreatmentCompoundDevelopmentalStageDiseaseStaging**DiseaseState**FamilyMemberGeographicLocationHistologyOrganismOrganismPartPhenotypeSequenceOntologyBioSequenceTypeStrainOrLineTargetedCellTypeTestTypeTumorGrading

## BioMaterialCharcteristics,

**BioMaterialCharcteristics,**
**Disease State**
- The name of the pathology
- diagnosed in the organism
-  from which the biomaterial
-  was derived.
- The disease state is normal
-  if no disease has been
- diagnosed.

**DiseaseStateDatabase**

## Ontology Entry in caArrayExternal (to the MGED ontology) controlled vocabulary or ontology that can be referred such as ICD-9 or Gene Ontology. 

## BioMaterialCharacteristics in caArray

## Slide 19

## caArray Data Portal &

- _Data Portal__:_ submission of original, raw data files with associated experiment and sample information (MIAME annotations).
- _Data analysis and visualization tools_:
- caBIG tools:
- caWorkbench - Columbia
- GenePattern – MIT/Broad
- DWD - UNC Lineberger
- GenePattern - MIT/Broad
- VISDA – Georgetown
- Cancer Molecular Pages – Burnham
- Function Express – Wash U Siteman
- webGenome - RTI

**caArray Data Portal &**
**Data Analysis Tools**

## caArray Configuration (federated)

_**caArray Configuration (federated)**_

- caGRID

## geWorkbench www.geworkbench.orgColumbia University, NY: Andrea Califano, Aris Floratos, Manjunath Kustagi

**Key Features:**
**Gene expression data**
- Support for several formats
- Seamless _integration with caArray_
- Rich collection of visualizers
- Filtering, normalization, analysis components
- Reconstruction and visualization of molecular interaction networks
**Sequence analysis**
- Sequence homology (BLAST, HMM, Smith-Waterman)
- Promoter analysis
- Motif discovery
- Synteny
**Annotations**
- Integration with CGAP gene annotations and BioCarta pathways
- Mapping to GO categories
**Development platform**
- Open source, Java-based
- Component architecture, facilitating customization

## GenePattern: A platform for integrative genomics

**65+ Modules**
**Microarray Analysis**
**Proteomics**
**Clustering**
**Marker selection**
**SNP/Copy #**
**Data visualization**

**PreprocessDataset**extract breast samples

- Ted Liefeld, Broad Inst.

- Graphical Environment

- Pipeline Environment

- Programming Environments

- Bicluster

- Heat Map

- Prediction Results

- # source("D:/CGP2003/GenePattern_modules/Golub_et_al_1999.R", echo = TRUE)
- #  GenePattern
- #
- #  Molecular Classification of Cancer: Class Prediction by Gene Expression
- #
- #  Summary: This R/GenePattern script implements the supervised prediction metho
- #           in Golub et al 1999, Science 286:531-537 (1999).
- # Load and set up GenePattern commands and server
- source("http://wilkins.wi.mit.edu:7070/gp/GenePattern.R", echo = FALSE, print.ev
- server <- SOAPServer("http://wilkins.wi.mit.edu", "/axis/servlet/AxisServlet", 7
- source(paste("http://", server@host, ":", server@port, "/gp/getAllTaskWrappers.j
- # Neighborhood analysis
- MS.out <-** MarkerSelection**("data.filename" = "http://www-genome.wi.mit.edu/mpr/pu
- 	  "class.filename" =“”
-       "pred.results.file" = "pred.results",
-       "data.results.file" = "data.results",
-       "num.permutations" = "25",
- file.show(MS.out$pred.results)
- file.show(MS.out$data.results.gct)
- data <- read.table(MS.out$pred.results, header=T, sep="\t", skip=14)

- GenePattern

**MAGE**** **
**caArray, file, or URL**

**MAGE-ML ImportViewer**
- Import MAGE-ML data

**ConvertToMAGEML**write out MAGE XMl file

**GeneNeighbors**compute nearest neighbors ofcyclin D1 in breast cells

**SelectFeaturesRows**get expression data for breast neighbors in ovary cells

**SelectFeaturesColumns**extract ovary samples

- Module Repository

- Task Integrator
- -add your own analyses methods to GenePattern

**MAGE**
**data file**

## caGrid 0.5.4 

**NCICB**
- 1. GUMS
- 2. CAMS
- 3. GME
- 4. caBIO
- 5. caArray
- 6. caMOD
- 7. SNP500Cancer
**         ****Duke**
-                 8. rProteomics
**Pittsburg Medical Center**
- 9. caTIES
**Lombardi - Georgetown**
- 10. PIR: proteomics inf. resource

**caGrid 0.5.4**** **
- current stable, supported,
- grid environment for caBIG
- Advertise, Query, and Discover services and data

## http://cagrid-browser.nci.nih.gov

**http://cagrid-browser.nci.nih.gov**

- _caGrid Development team:_
- Ohio State University
- NCICB
- SAIC, Oracle, Panther Informatics,
- TerpSys, BAH

## CTMS Workspace

- Discovery utilizing caBIGTM
- Integrated Cancer Research Tools

## NCICB Download http://ncicb.nci.nih.gov/download & GForge site http://gforge.nci.nih.gov

## Training and Support ncicb@pop.nci.nih.gov 

## Acknowledgements

- caArray team:
- Mervi Heiskanen (NCI)
- Anand Basu (NCI)
- Xiaopeng Bian (NCI)
- Rob Daly  (5am Solutions)
- Eric Tavela (5am Solutions)
- Leslie Power (5am Solutions)
- Steven Matyas (5am Solutions)
- Jerry Eads (NTVI)

- EVS team:
-    Gilberto Fragoso (NCI)
-    Frank Hartel (NCI)
- Training & Support:
-     Don Swan (TerpSys)
- caBIG Developers &
- Adopters

- heiskame&mail.nih.gov