Atlas Interoperablity I & II:progress to date,

requirements gathering

Session I: 8:30 – 10am

Session II: 10:15 – 12pm

Interoperability requirements

• The big question:– Do the observed relationships hold across species

(development phases, etc.)

It is a component of community building

• What types of bridges we may build– Brain region homology (impossible?? Messy…)

• Topology, shape, metric relationships

– Functional homology– Neurochemical homology– Developmental homology

How to build these bridges• Ontologies:

– Neuronames, UMLS, BIRNLex, BONFIRE…– Ontology alignments: a high priority action item– Standard ontology formats and shared ontology tools (BIRNLex)

• Coordinate systems– Absolute: Stereotaxic (which), Talairach– Coordinate translation services (??) working within species– Other types of location description (relative, ontology-based,

expression)• Standard formats and APIs

– To access data– To query registries (metadata, ontologies, spatial, cross-walks) – To exchange data across atlases– To perform analysis:

• Find automatic segmentation tools (cells and tissues) and morphometric analysis tools (incl. cell counting and volumetric analysis)

– To allow conceptual interoperability (across concepts used in different species)

Progress has been made…• On the mouse brain atlas front end and query

framework (MBAT’2007), 3D slicer, query atlas, human brain atlas

• On data preparation and upload: warping tools, HID/AID

• On the ontology front:– Formal management of ontologies and Bonfire

translations; concept mapper, concept queries• On the spatial front

– Spatial alignment and registration (spatial registry), spatial query, multi-scale visualization

• Annotations– Combined spatial-semantic

• On cross-atlas interoperability (Atlas Interop API)

Concept Query InfrastructureUCLA & CC

Term Source

Database

Search the DB at that column for results

matching the query

Mediator

Gives the column and DB information

matched to that TERM(BIRNLex/Bonfire)

•Holds some underlying “business logic” for the Query interface-categorizes data types and search criteria. •Move functionality to this over time

Example: User generates a query for calb1 in C57BL/6 in BIRN Microarray DB and GeneNetwork.

(Metadata Database holds information for Interface to formulate Query)

1) Query Term Source DB for terminology from different sources calb1 and C57BL/6, and the output is:

DATASOURCE = TABLE NAME FIELD NAME Gene Network = “Gene table” “genesymbol” Microarray = “UAD_probe_term” “key=GeneSymbol”2) (Optional) Query Term Source DB for all fields of

“Table Name”3) To generate this query to mediator, “get all fields =

Calb1 in the given Table of each DataSource”4) Query mediator for all the matching results

This infrastructure:• Easier for the User• Expandable• More comprehensive searches of

multiple sources• Start migrating functionality from

Interface to Server after Fall• If on Server, easier for others to

use our infrastructure

MetadataDatabase

Concept Query Interface

Spatial Query InfrastructureUCLA & UCSD

ImageMetadata

Retrieve Images

Spatial Registry

ArcIMS Images

Atlas API

webservices

Atlas Interoperabiity Server

Atla

s A

PI

• User can query with ROI• Uses Atlas

Interoperability Server and API

• Visualize images using zViewer

zViewer 2D images (integrated into a MBAT window)

Spatial QueryInterface

InformationSources

Information Query UCLA, USC, CC

BAMs

BonFire

JDBC

webservicesMediator/webservices

User can query two different information sources depending on needs of user

1) ontologies: useful for defining what a user means by a term and mapping data across data sources

2) BAMs information: connections, molecules and cells in different areas (It is unlikely we will have the needed time to make the necessary changes to expand this by the fall release-we will need to decide if we want to include it at all)

Information Query Interface

MicroarrayDatabases

GeneNetwork

Microarray Data Handling UCLA, UTHSC

BIRN Microarray

MicroarrayUpload

Interface

MicroarrayUpload

Interface

BarlowDatabase

SmithDatabase

MAGE XML

New 2007:• MAGE compatible-facilitates

compatibility with other microarray sources

• Expanded Query of DBs• More easily expandable to

other sources• More robust process• More flexible queries

Gene Expression Explorer (can be visualized in MBAT)

URL access

Mediator/webservices

Microarray

Annotation

Database

Concept Query Interface

ImageMetadata

Retrieve Images

Spatial Registry

2D Image Data Handling UCLA, UCSD (CCDB, ArcIMS), Neurcommons (ABA), Stott Parker (Gensat)

ArcIMS Images

Atlas API

ABA Images

Gensat Images

CCDB images

Neuro-Commons

Implement Stott’s

Gensat DB

CCDB

webservices

RDF/Sparql

webservices

URL accessMediator/webservices

2DRegistrationWorkflow

2DRegistrationWorkflow

zViewer 2D images (integrated into a MBAT window)

User can query by Concept or Spatial Query and visualize in zViewer

Concept Query Interface

Spatial QueryInterface Atlas

Interoperabiity Server

Handling multiscale images

Spatial-Semantic Annotation

DEMO

Spatial Registration

DEMO

Arbitrary query of spatially distributed signals

State Exchange between SA-MBAT

Atlas alignment problems… Transformation matrix wrapped in Coordinate Transformation Service

Additional desiderata

• Additional data types– Histopathology– Time series

• Anatomical• Physiological

– Behavior– Connectivity (wiring/microwiring)– Data from typical laboratory

• Standards in metadata, registration procedures, middleware tools, handling of ontologies, annotations, etc.

Human and Rodent Atlasing:what is in common, what is different

Significant overlap in needs and functionality:• Atlases in two roles: as the query/analysis framework,

and as spatial/semantic data registration framework• Handling large images, using specialized grid tools• Creating, registering, managing and querying 3D

reconstructions• 2D Image registration: from common metadata

registration to spatial registration to semantic registration• Coordinate systems, and location exchange between

atlases• Image and 3D annotation • Vocabularies/ontologies

Human and Rodent Atlasing:what is in common, what is different

Differences:• Data types: 2D vs 3D and reconstructions• Upload and registration: Regular process of image

acquisition and registration, vs multiple acquisition methods, metadata conventions and resolutions, multi-scale --> multiple atlas tools

• Multi-scale image registration, query and visualization• Query: Queries defined by clinical needs; medical

records connection• Regulatory: de-identification • Also: formats; ontology stores we connect to; diseases;• Analytically-driven vs data registration/fetching-driven• Canonical vs individual

We come from different contexts – but let’s not duplicate where possible!

Panel discussion• What is atlas interoperability in your domain?• What are interoperability challenges and

priorities?• How tools and approaches from other testbeds

can be re-used?• What additional questions you would like to

formulate once data and services from other testbeds become available?

• What could be immediate steps towards BIRN mashups?

• Now that we have MBAT, Slicer, Query Atlas, DTI & LDDMM – what is next?

Slicer

• Within the user interaction threshold – updating label and eventually concept in ontology – this could be an excellent semantic bridge to other atlases

• Connectivity data– Searching based on connectivity neighborhood

• Need a database of visual maps, and then pattern matching with the existing maps– Structural and functional connectivity

Canonical Atlas?

• In Human: atlas is a set of priors and not a canonical atlas

• Mouse: atlas is essentially a paper atlas over a new media, and a common framework

• Connecting atlases based on variation

Path forward• Fetching data

– How much to fetch, from control and target– Analysis of stat significance (from Slicer)– Disease models used in mouse – populate them with data; need

to get human to mouse and mouse to human use cases (conceptua interperability?)

• For example:– AD: reduction of neuroactivation, esp. in temporal regions of

hippocampus (Function BIRN so far focused on regions without strong homology to mouse): what genes in mouse (Genesat, ABA) – have expressions in ventral Hippocampus, and related connectivity differences;

– APP expressions (overexpressed in ventral hippocampus) – AD– APOE as a potential cortical factor, in AD; what other genes co-vary

with APOE

• Refinement of queries• Visualization at different levels• Adding analysis functions to atlases

– E.g. counting axons, spines– E.g. comparing histograms, for different signals, different areas

The use case

• Search genes co-expressed with Apoe (highlighted in human studies) using ABA in hippocampus using NeuroBLAST

• Issues:– Poor data on connections

• Both nice graphics and interactive display

– Ontology alignment– Adding analysis to mouse