Transcript Ontologies for Gene Expression

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Ontologies for Gene Expression
 History
of ontologies in bioinformatics
 BioOntologies Consortium
 Ontologies
for the biochemical networks that
control gene expression
Ontologies
 Clear
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thinking about how to structure information
 Clearly
understand each field in a database
 Formal and informal definitions for database
elements
 Type of value, range of values
 Product field of Gene class can be a Protein or an RNA
 Ability
to enforce data correctness
 Ability to compute with database elements in a
reliable fashion
History of Ontologies in
Bioinformatics
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 1994
Meeting on Interoperation of Molecular
Biology Databases (MIMBD-94)
 BioOntologies
meetings in 1997, 1998, 1999, 2000,
2001
 Ontology tutorials at ISMB conference
 BioOntologies Consortium
BioOntologies Consortium
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 Concerned
with ontology infrastructure for
bioinformatics
 Exchange
of ontologies
 Beware: All bioinformatics ontologies expressed in different
ontology language
 Software
for constructing, interpreting, applying
ontologies
 http://bioontology.ingenuity.com/
BioOntologies Consortium
 ISMB-2000
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paper evaluating ontology exchange
languages for bioinformatics
BioOntologies Consortium
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 ISMB-2000
paper evaluating ontology exchange
languages for bioinformatics
 Define criteria for evaluating existing languages
 No existing languages satisfy all criteria
 Desired: XML syntax, frame semantics
 1999:
Karp and Chaudhri develop XOL language
 2000:
OIL/DAML succeeds XOL
BioOntologies Consortium –
Potential Interactions
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 Standards
and tools
 DAML/OIL
 SRI’s GKB Editor ontology editor
 Collaborate
 Post
on ontology development
ontologies on BioOntologies web site
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Be Precise About Ontology Uses
 Data
submission
 Data exchange among databases
 High-level database design
 Mapping
from ontologies to database
management systems essential
 Beware of flatfiles
 Beware of XML
ArrayExpress
 Ontology
 MAML
 SQL
for specifying experiments
import and export
query access
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EcoCyc Project Overview


E. coli Encyclopedia and model organism database
 Tracks the evolving annotation of the E. coli genome
 Over 3000 literature citations
Collaborative development via internet
 Karp (SRI) -- Bioinformatics architect
 Riley (MBL) -- Metabolic pathways, signal transduction
 Saier (UCSD) and Paulsen (TIGR)-- Transport
 Collado (UNAM)-- Regulation of gene expression

Ontology: 1000 biological classes
Database content: 16,000 instances

Over 3,300 registered users

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Encoding Transcriptional
Regulation in EcoCyc -- Goals




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Capture transcriptional regulatory mechanisms within a well
structured ontology
Provide a training set for inference of gene networks
Interpret gene-expression datasets in the context of known
regulatory mechanisms
Compute with regulatory mechanisms and pathways
 Summary statistics
 Pattern discovery
 Complex queries
 Consistency checking
Pathway Tools Extensions
for Transcriptional Regulation
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 Integration
of RegulonDB (Collado et al.)
 Regulation
ontology
 Editing
 New
tools for regulatory interactions
visualizations
EcoCyc Ontology for
Transcriptional Regulation
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 Terminology:
Transcription Unit
 Definition: A set of coding regions and associated control
regions that yield a single transcript
 “Operons” must have more than one gene
 Prokaryotic terminology
 Key
features of ontology
 Model gross structure of transcription units, transcription
factors, RNA polymerase
 Model all molecular interactions as biochemical reactions


Binding of transcription factors to ligands and to DNA sites
Binding of RNA polymerase to promoter
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Ontology for Transcriptional
Regulation – Current Limitations
 Focused
on prokaryotic regulation
 Mechanisms
based on control of transcription
initiation only, e.g., no attenuation
Ontology for Regulatory
Interactions
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 Common
slots
 Citations, Comment, Common-Name, Synonyms
 Class DNA-Regions
 Left-End-Position, Right-End-Position, Relative-StartDistance
 Class Transcription-Units


Components (Promoter, transcription-factor binding sites, genes,
terminator)
Class Promoters



Component-Of
Promoter-Strength-Exp, Promoter-Strength-Seq
Promoter-Evidence
Ontology for Regulatory
Interactions

Class DNA-Binding-Sites
 Component-Of
 Regulated-Promoter, Relative-Center-Distance
 Type-Of-Evidence

Classes Protein-Complexes, Polypeptides
 Components / Component-Of

Class Binding-Reactions
 Reactants
 Activators
 Inhibitors
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EcoCyc Ontology for
Transcriptional Regulation
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 One
DB object defined for each biological entity
and for each molecular interaction
trp
apoTrpR
trpLEDCBA
Int005
site001
Int001
pro001
Int003
trpL
trpE
trpD
trpC
trpB
trpA
TrpR*trp
RpoSig70
Integration of RegulonDB
 RegulonDB
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has been loaded into EcoCyc
 RegulonDB originally relational
 Lisp loader tools developed for relational table dumps
 Statistics:
528 transcription units
 620 promoters
 617 DNA binding sites
 83 transcription factors

Consistency Checks on
RegulonDB Data
 Find
transcription units containing:
 Undefined components
 No gene components
 Genes that are not contiguous
 Genes with conflicting transcription directions
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Interactive Editing Tools
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 SRI
created interactive tools for creating and
modifying regulatory mechanisms
 Ongoing
updates to RegulonDB occur in EcoCyc
Visualization Capabilities
 Transcription
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units
 Transcription unit containing a gene: araA
 Details of a transcription unit
 Regulons: CRP, NARL
 Pathway control
 Overview: show rxns controlled by a TF (CRP, FNR), show
other rxns controlled by same TF(s) (use a rxn in purine
biosyn)
Characterization of the E. coli
Genetic Network
 551
transcription units include 1115 (25%) genes
 Controlled
 All
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by 86 transcription factors
experimentally determined
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Genes per Transcription Unit
250
233
Number of transcription units
200
144
150
100
76
50
40
26
6
8
6
7
4
5
2
2
1
2
0
2
8
9
10
11
12
13
14
15
0
1
2
3
4
5
Number of genes per transcription unit
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Binding Sites per Transcription Unit
160
144
140
Number of transcription units
120
100
80
63
60
47
40
20
20
10
10
5
2
2
1
1
1
8
9
10
11
12
0
1
2
3
4
5
6
7
Number of binding sites per transcription unit
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Transcription Factor Reach
30
26
Number of transcription factors
25
20
20
15
9
10
7
6
5
2
2
6
7
1
2
2
9
11
1
1
1
1
1
1
14
16
18
24
25
28
2
1
0
1
2
3
4
5
8
Num ber of transcription units regulated by given transcription factor
31
95
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Transcription Units per Pathway
30
25
Number of pathways
25
20
15
10
6
4
5
2
0
0
0
5
6
7
1
0
1
2
3
4
Num ber of operons per pathw ay
8
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Pathways per Transcription Unit
400
350
343 343
Number of operons
300
250
192
200
150
102
100
56
39
50
16
7
3
1
0
0
0
1
2
3
4
5
Num ber of pathw ays in an operon
6
7
Visualization of the Full
E. coli Genetic Network
 Influences
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of transcription factors on other
transcription factors
 50 of 85 TFs do not affect other TFs
 Maximum network depth of 3
 Only CRP has a branching factor greater than 2
 No feedback loops other than autoregulation
 Negative auto-regulation is the dominant form of
feedback