Analysis Environments For Scientific Communities From Bases to Spaces Bruce R. Schatz Institute for Genomic Biology University of Illinois at Urbana-Champaign Baker Center for Bioinformatics Iowa State University October 6, 2006

What are Analysis Environments Functional Analysis Find the underlying Mechanisms Of Genes, Behaviors, Diseases Comparative Analysis Top-down data mining (vs Bottom-up) Multiple Sources especially literature

Building Analysis Environments Manual by Humans Interactionuser navigation Classificationcollection indexing Automatic by Computers Federationsearch bridges Integrationresults links

Trends in Analysis Environments Central versus Distributed Viewpoints The 90s Pre-Genome Entrez (NIH NCBI) versus WCS (NSF Arizona) The 00s Post-Genome GO (NIH curators) versus BeeSpace (NSF Illinois)

Pre-Genome Environments Focused on Syntax pre-Web WCS (Worm Community System) Search words across sources Follow links across sources Words automatic, Links manual Towards Integrated Searching

Post-Genome Environments Focused on Semantics post-Web BeeSpace (Honey Bee Inter Space) Navigate concepts across sources Integrate data across sources Concepts automatic, Links automatic Towards Conceptual Navigation

Worm Community System WCS Information: Literature BIOSIS, MEDLINE, newsletters, meetings Data Genes, Maps, Sequences, strains, cells WCS Functionality Browsingsearch, navigation Filteringselection, analysis Sharinglinking, publishing WCS: 250 users at 50 labs across Internet (1991)

WCS Molecular

WCS Cellular

WCS invokes gm

WCS vis-à-vis acedb

from Objects to Concepts from Syntax to Semantics Infrastructure is Interaction with Abstraction Internet is packet transmission across computers Interspace is concept navigation across repositories Towards the Interspace

THE THIRD WAVE OF NET EVOLUTION PACKETS OBJECTS CONCEPTS

Technology Engineering Electrical FORMAL INFORMAL (manual) (automatic) IEEE communities groups individuals LEVELS OF INDEXES

Post-Genome Informatics I Comparative Analysis within the Dry Lab of Biological Knowledge Classical Organisms have Genetic Descriptions. There will be NO more classical organisms beyond Mice and Men, Worms and Flies, Yeasts and Weeds. Must use comparative genomics on classical organisms Via sequence homologies and literature analysis.

Post-Genome Informatics II Functional Analysis within the Dry Lab of Biological Knowledge Automatic annotation of genes to standard classifications, e.g. Gene Ontology via homology on computed protein sequences. Automatic analysis of functions to scientific literature, e.g. concept spaces via text extractions. Thus must use functions in literature descriptions.

Informatics: From Bases to Spaces data Bases support genome data e.g. FlyBase has sequences and maps Genes annotated by GeneOntology and linked to biological literature information Spaces support biological literature e.g. BeeSpace uses automatically generated conceptual relationships to navigate functions

BeeSpace FIBR Project BeeSpace project is NSF FIBR flagship Frontiers Integrative Biological Research, $5M for 5 years at University of Illinois Analyzing Nature and Nurture in Societal Roles using honey bee as model (Functional Analysis of Social Behavior) Genomic technologies in wet lab and dry lab Bee Bee [Biology] gene expressions Space Space [Informatics] concept navigations

System Architecture

Concept Navigation in BeeSpace

V1 BeeSpace Community Collections Organism Honey Bee / Fruit Fly Song Bird / Soy Bean Behavior Social / Territorial Foraging / Nesting Development Behavioral Maturation Insect Development Insect Communication Structure Fly Genetics / Fly Biochemistry Fly Physiology / Insect Neurophysiology

CONCEPT SWITCHING “Concept” versus “Term” set of “semantically” equivalent terms Concept switching region to region (set to set) match term Semantic region Concept Space

BeeSpace Analysis Environment Build Concept Space of Biomedical Literature for Functional Analysis of Bee Genes -Partition Literature into Community Collections -Extract and Index Concepts within Collections -Navigate Concepts within Documents -Follow Links from Documents into Databases Locate Candidate Genes in Related Literatures then follow links into Genome Databases

Well Characterized Gene

Poorly Characterized Gene

Gene Summarization, BeeSpace V2

Collaboration across Users

Category Browse (Collection)

Category Browse (Search)

PlantSpace Examples

Interactive Functional Analysis BeeSpace will enable users to navigate a uniform space of diverse databases and literature sources for hypothesis development and testing, with a software system beyond a searchable database, using literature analyses to discover functional relationships between genes and behavior. Genes to Behaviors Behaviors to Genes Concepts to Concepts Clusters to Clusters Navigation across Sources

BeeSpace Information Sources General for All Spaces: Scientific Literature -Medline, Biosis, CAB Abstracts Genome Databases -GenBank, ProteinDataBank, ArrayExpress Special for BeeSpace : Model Organisms (heredity) -Gene Descriptions (FlyBase, WormBase) Natural Histories (environment) -BeeKeeping Books (Cornell, Harvard)

XSpace Information Sources Organize Genome Databases (XBase) Compute Gene Descriptions from Model Organisms Partition Scientific Literature for Organism X Compute XSpace using Semantic Indexing Boost the Functional Analysis from Special Sources Collecting Useful Data about Natural Histories e.g. CowSpace Leverage in AIPL Databases

Towards SoySpace Organize Genome Databases (SoyBase) Partition Scientific Literature for SoyBean Gene Descriptions from Models (TAIR) Natural Histories from Population Databases Key to Functional Analysis is Special Sources Collecting Appropriate Text about Genes Extracting Adequate Data about Histories Leverage is National Archives of germplasm and Historical Records for soybean crops

Towards the Interspace The Analysis Environment technology is GENERAL ! BirdSpace? BeeSpace? PigSpace? CowSpace? BehaviorSpace? BrainSpace? SoySpace? PlantSpace? BioSpace … Interspace