Analysis Environments For Scientific Communities From Bases to Spaces

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Analysis Environments For Scientific
CommunitiesFrom Bases to Spaces
Bruce R. Schatz Institute for Genomic
Biology University of Illinois at
Urbana-Champaign schatz_at_uiuc.edu,www.beespace.uiuc
.edu
Baker Center for Bioinformatics Iowa State
University October 6, 2006
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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

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Building Analysis Environments

• Manual by Humans
• Interaction user navigation
• Classification collection indexing
• Automatic by Computers
• Federation search bridges
• Integration results links

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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)

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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

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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

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Worm Community System

• WCS Information
• Literature BIOSIS, MEDLINE, newsletters,
  meetings
• Data Genes, Maps, Sequences, strains, cells
• WCS Functionality
• Browsing search, navigation
• Filtering selection, analysis
• Sharing linking, publishing
• WCS 250 users at 50 labs across Internet (1991)

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WCS Molecular
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WCS Cellular
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WCS invokes gm
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WCS vis-à-vis acedb
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Towards the Interspace

• 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
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THE THIRD WAVE OF NET EVOLUTION
CONCEPTS
OBJECTS
PACKETS
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LEVELS OF INDEXES
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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.

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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.

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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

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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 Biology gene expressions
• Space Informatics concept navigations

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System Architecture

• BeeSpace

• Concepts

• Concepts

• SEQ

• Expressions

• Expressions

• Databases

• Bees

• Flies

• Documents

• Documents

• SEQ

• Community

• Community

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Concept Navigation in BeeSpace
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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

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CONCEPT SWITCHING

• Concept versus Term
• set of semantically equivalent terms
• Concept switching
• region to region (set to set) match

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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

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Well Characterized Gene

• Ling et. al., PSB 2006

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Poorly Characterized Gene

• Ling et. al., PSB 2006

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Gene Summarization, BeeSpace V2
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Collaboration across Users
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Category Browse (Collection)
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Category Browse (Search)
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PlantSpace Examples
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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

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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)

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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

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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

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Towards the Interspace

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