Transcript THE INTERSPACE PROTOTYPE An Analysis Environment for

BeeSpace:
An Interactive Environment for Analyzing
Nature and Nurture in Societal Roles
Bruce Schatz
Institute for Genomic Biology
University of Illinois at Urbana-Champaign
www.beespace.uiuc.edu
Third Annual Project Workshop
IGB, Urbana IL
May 21, 2007
BeeSpace Workshop Schedule

Introductory Lectures (Bevier Auditorium), 9-12



Working Sessions (IGB Training Rooms), 1-5



Informatics, Biology, Education
Faculty Investigators across Campus
System Demo, Biology Usage, User Support
Staff Members within IGB
Strategic Planning (IGB Conference Rooms),9-12

Project Members and Visitors
BeeSpace is…

A Big Interdisciplinary Project
The First and the Biggest at IGB
 NSF FIBR $5M
2004-2009


General Biotechnology (Dry Lab)


Interactive Environment for
Functional Analysis (Bioinformatics)
Important Science (Wet Lab)

Model Dissection of Nature-Nurture
(Genomics of Behavioral Plasticity)
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
Project Investigators
Biology
Gene Robinson, Integrative Biology (genomics)
Susan Fahrbach, Biology at Wake Forest (anatomy)
Sandra Rodriguez-Zas, Animal Sciences (data analysis)
Informatics
Bruce Schatz, Medical Information Science (systems)
ChengXiang Zhai, Computer Science (text analysis)
Chip Bruce, Library & Information Science (users)
Collaborators
FlyBase, BeeBase, Bee Genome Community
BeeSpace Goals
Analyze the relative contributions of
Nature and Nurture in
Societal Roles in Honey Bees
Experimentally measure gene expression in the brain for
important societal roles during normal behavior
varying heredity (nature) and environment (nurture)
Interactively annotate functions for differential expression
using concept-based navigation of biological literature
and gene –centered summarization analysis
for Social Beehavior
Complex Systems I
Understanding Social Behavior




Honey Bees have only 1 million neurons
Yet…
A Worker Bee exhibits Social Behavior!
She forages when she is not hungry
but the Hive is
She fights when she is not threatened
but the Hive is
for Functional Analysis
Complex Systems II
Understanding Functional Analysis




Integrating many sources to explain behavior
Across organisms and functions
Most of functional explanations are in text
Text Mining and Gene Summarizing
Intersecting Multiple Viewpoints to
Discover Emergent Properties
Post-Genome Informatics
Comparative Genomics to Classical Models

Sequence-based gene annotation


To standard classifications such as Gene Ontology
Literature-based gene annotation

To computed classifications via extracted concepts
Descriptions in Literature MUST be used in future
interactive environments for functional analysis!
Informatics: From Bases to Spaces
data Bases support genome data
e.g. FlyBase has sequences and maps
Insect genes typically re-use Drosophila names.
BeeBase (Christine Elsik, Texas A&M)
Uses computed orthologs to annotate genes
information Spaces support biological literature
BeeSpace uses automatically generated
conceptual relationships to navigate functions
System Architecture
Concept Navigation in BeeSpace
Behavioral
Biologist
Bee
Literature
Molecular
Biology
Literature
Brain Gene
Expression
Profiles
Brain Region
Localization
Neuroscience
Literature
Neuroscientist
Molecular
Biologist
Bee
Genome
Flybase,
WormBase
BeeSpace General Biotechnology
Bioinformatics of Genes and Behavior
Using scalable semantics technology





Using General Expressions and Literatures
Annotation Pipelines from Sequence and Text
Creating and Merging multiple SPACES
Where REGIONS are semantically created
And useful regions become shared spaces
BeeSpace Community Collections

Organism



Behavior



Social / Territorial
Foraging / Nesting
Development




Honey Bee / Fruit Fly
Song Bird / Soy Bean
Behavioral Maturation
Insect Development
Insect Communication
Structure


Fly Genetics / Fly Biochemistry
Fly Physiology / Insect Neurophysiology
Analysis Environment: Model
Explicitly capture SCIENCE in SYSTEM!
Wet Lab:
Locate Candidate Genes
Classify Differential Genes
Dry Lab:
Locate Candidate Texts
Classify Differential Texts
Analysis Environment: Features
SPACE is a Paradigm not a Metaphor!
Point of View for YOUR Problem
Externally:
-Dynamically describe custom Region of Space
-Merge Regions to form Hypothesis Space
-Differentially express genes against Space
Analysis Environment: System
Concepts and Genes are Universal Entities!
Uniformly Represented
Uniformly Manipulated
Internally:
-Extract and Index Concepts within Collections
-Navigate Concepts within Documents
-Follow Genes from Documents into Databases
CONCEPT SWITCHING

“Concept” versus “Term”


set of “semantically” equivalent terms
Concept switching

region to region (set to set) match
Semantic region
term
Concept Space
Concept Space
BeeSpace Information Sources
General for All Spaces:
Scientific Literature
-Medline, Biosis, Agricola, Agris, CAB Abstracts
-partitioned by organisms and by functions

Model Organisms
-Gene Descriptions (FlyBase, WormBase, MGI, OMIM,
TAIR, SCD)

Special Sources for BeeSpace:
-Natural History Books (Cornell Library, Harvard Press)
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. PigSpace Leverage in USDA Databases
Towards the Interspace
The Analysis Environment technology is
GENERAL!
BirdSpace? BeeSpace?
PigSpace? CowSpace?
SoySpace? CornSpace?
InsectSpace? PlantSpace?
BioSpace? MedSpace?
Biology: The Model Organism
Western Honey Bee, Apis mellifera
A model for social behavior
Emergent Properties
Complex Behavior from Simple Model





Normal Behavior – honey bees live in the wild
Controllable Heredity – Queens and Hormones
Controllable Environment – hives can be modified
Small size manageable with genomic technology
Differential genes for normal behavior
Nature and Nurture both act on the genome
Heredity
Environment
Power of Social Evolution



Agriculture (bee forager)
Warfare
(bee defender)
Language (bee dancer)
Humans do These, So do Social Insects
We are performing Nature-Nurture dissection
to locate candidate genes spanning these
normal behaviors of honey bees
(Whitfield et al, 2002)
Experimental Status




Genome Complete and Microarray Fabricated
Bees collected for Societal Role experiments
Initial Dissections complete on EST array
On-going first Genome Array dissection

Sequence Annotation Pipeline being used
Literature Annotation Pipeline being tested

Designing Meta-analysis Environment

Education: Scientific Inquiry

Graduate



Undergraduate


New Research via Functional Analysis
5 early adopter labs, then 15 international labs
New Bioinformatics Course using BeeSpace
High School

Integrate into Field Biology course at Uni High