Ondex: Data integration and visualisation

Ondex – Data integration and visualisation
Catherine Canevet
Rothamsted Research
London Biogeeks – May Tech Meet

Rothamsted Research
North Wyke
Largest agricultural and crop science research institute in UK
Almost certainly the oldest in the world (started in 1843)
350 Scientific staff
Open weekend May 22nd-23rd 11am-5pm
www.rothamsted.ac.uk/openweekend/

Outline
Data integration in Systems Biology
Data integration in Ondex
Data visualisation in Ondex and application cases

Opportunities and challenges for modelling
Large amounts of multi-’omics data
Genomics, transcriptomics, proteomics, metabolomics, …
The biological systems span multiple levels of biological organisation
Non-trivial to integrate the data
 2 main challenges

Syntactic integration challenge
Over 1000 databases freely available to public
Over 60 million sequences in GenBank
Over 870 complete genomes and many ongoing projects
Over 17 million citations in PubMed
PubMed growth by 600,000 publications each year
Integration of Life Science data sources is essential for Systems Biology research
http://www.ncbi.nlm.nih.gov/Database

Ear
Semantic Integration challenge
Same concept different names
Synonyms
Same name different concepts
Homographs

Outline

Everything is a network

Concepts and relations (1/2)
interact
Cell
Protein – Protein interaction network (PPI)
Cellular location of proteins
Protein
Protein
e.g. Network of Concepts and Relations
RelationType
interact
located in
ConceptClass
ConceptClass
Protein
CelComp
Protein
Protein
Properties: compound name, protein sequence, protein structure, cellular component, KM-value, PH optimum …
Ontology of Concept Classes, Relation Types and additional Properties

Reaction
Reaction
produced by
consumed by
consumed by
produced by
Metabolite
Metabolite
Metabolite
Concepts and relations (2/2)
Transformation to binary graph
Properties: compound name, protein sequence, protein structure, cellular component, KM-value, PH optimum …
Concepts:
Relations:

Data Integration
Data Input
Graph of concepts and relations
Biological Databases
Import
Ontologies & Free Text
Data alignment
Concept mapping
Sequence analysis
Text mining
Experimental Data

Importing data into Ondex
What databases to import
What format these are in
Ondex parsers already written
Generic
OBO, PSI-MI, SBML, Tab-delimited, Fasta
Database-specific
Aracyc, AtRegNet, BioCyc, BioGRID, Brenda, Drastic, EcoCyc, GO, GOA, Gramene, Grassius, KEGG, Medline, MetaCyc, Oglycbase, OMIM, PDB, Pfam, SGD, TAIR, TIGR, Transfac, Transpath, UniProt, WGS, WordNet

Example of resulting graph
Has similar sequence
Target sequence
Binds to, has similar sequence
Repressed by, regulated by, activated by
Member is part of
Gene
Protein
Encoded by
Is_a
Member is part of
Is_a
Transcription factor
Is_a
Member is part of
Enzyme
Protein complex
Is_a
catalyses
Catalysing class
Member is part of
Reaction
Member is part of
EC
Is_a
Pathway

Ondex Data Integration Scheme
Treatments from DRASTIC
Graph alignment
Pathways from KEGG
Data input& transformation
Data integration
Visualisation
Clients/Tools
Heterogeneous
data sources
Ondexgraph warehouse
Integration
Methods
Ondex
Visualization
Tool Kit
UniProt
Accession
Generalized Object Data Model
Database Layer
Parser
Name based
Web Client
AraCyc
Parser
Transitive
Taverna
KEGG
Blast
Parser
ProteinFamily
Transfac
Data Exchange
Parser
Pfam2GO
OXL/RDF
Microarray
Lucene
Parser
Web Service

Semantic Integration by Graph Alignment
Create relations between equivalent entries from different data sources
Identified by mapping methods
Concept accessions (UniProt ID)
Concept name (gene name), synonyms
Sequence methods
Graph neighbourhood
Text mining

Outline

Ondex network – more than a visual dump of a database …
Addressed issue of interrelatedness of databases
Complexity of interactions
PPI, co-expression,
co-citation, …
Bring together data, exploit graph structure
Candidate gene prioritisation and pathway discovery
 Use Ondex tools (filters, annotators, layouts …)

Filters
Integrating different datasets
 large resulting graph
Need to narrow down
Select meaningful areas of the graph
Example in Ondex
protein-protein interaction network

Filters in Ondex
Protein protein interactions measured using quantitative techniques
Relations on graph have confidence values (confidence)
Threshold filter

Application case 1: Predicting fungal pathogenicity genes
Reference database of virulence and pathogenicity genes validated by gene disruption experiments
Literature mining
http://www.phi-base.org/
Sequence comparison – orthology and gene cluster analysis

http://www.phi-base.org/
List of “hot” target genes curated from literature
Loss of pathogenicity
Reduced virulence
Only genes validated by gene disruption experiments

ONDEX
Fusariumgraminearum
(microscopic fungus)
genome
FASTA
OXL
tab file
OXL
clusters of orthologs and paralogs between entries of PHI-base and Fusariumgraminearum
tab separated text file of clusters loaded in Excel
Inparanoid
mapping
Ondex front-end
Integrated phenotype and comparative genome information

Integrated phenotype and comparative genome information

Annotators (1/3)
Visualise concepts and relations using their attributes/properties
Colour
Shape
Size

Andrew Beacham
Mixed phenotype cluster
Reduced
virulence
Shape Legend
Star: animal
Circle: plant
Red square: Fusarium
Uninfected
Wild-type
PH-1
FGSG_9908
pkar PH-1
Protein kinase A- regulatory subunit

Annotators (2/3)
Virtual Knock-out
Annotator to see how important a single concept is to all possible paths contained in a network
Ondex resizes the concepts based on this score
Scale Concept by Value
Pie charts
Up/down regulation is indicated in red/green

AraCyc
ONDEX
Application case2: Mapping microarray expression data to integrated pathways
Parser
tab file
Arabidopsis C/N uptake
OXL
tab file
Jan Taubert
Accession based
Mapping
usingTAIR IDs
Ondex Interactive exploration
Enriched spreadsheet, e.g. AraCyc pathways

Annotators (3/3)
Run network statistics such as:
Connectivity
Centrality
Clustering
Network diameter
 Add annotation to the graph

Application case 3: Arabidopsis PPI network
Artem Lysenko
IntAct
TAIR
BioGRID
 Mapping the 3 databases based on TAIR accessions

Adding 3 sources of evidence
co-expression
sequence similarity
co-occurrence in scientific literature
 facilitate the identification of functionally related groups of proteins

Added attributes to nodes/edges
Network stats
Betweenness centrality (BWC)
 How influential (bridge)
Degree centrality (DC)
 Hub likeness
Markov Clustering
Identifies strongly connected groups of proteins in the network

Ondex annotator to visualise calculated centrality measures
Identify ‘influential’ nodes with low degree and high betweenness
Degree centrality repr. by node size
Betweenness centrality repr. by node colour
Artem Lysenko

Filters, annotators and layouts
Combination of these three types of tools in Ondex
 a more complex application case …

Application case 4: Bioenergy Project
Use bioinformatics to support phenotype-genotype research in bioenergy crops
Given a phenotypic variant is it possible to pin down the relevant genes?
Develop tools to support systematic analysis of QTL regions to pin down relevant genes
Identify genes implicated in biomass production in willow
Prioritise genes for experimental validation
Keywan Hassani-Pak
Biofuel Conversion Process
http://www.jgi.doe.gov/education/bioenergy/bioenergy_1.html

QTL and Genomic Data
QTL
Willow genome is not sequenced yetQTL may encompass many potentialcandidates, perhaps hundreds
Poplar is the first tree with fully sequenced genome
19 Chromosomes, 45778 predicted genes
4x larger than Arabidopsis genome
Not much known about the function of the genes

Linking genes to data sources
Linked
References
model
e.g. Poplar, Arabidopsis
Willow
Pathways
Plant Hormones
QTL Map
Orthologous
Markers
Physical
map
Expression Patterns
Genes
Gene Function
List of candidate genes linked to biological processes

Relevant Data Sources
Release 15.10
Poplar Gene Prediction v2.0 (Jan 2010)
All plants: 739,396 proteins
Reviewed: 28,404 proteins (3,84%)
PoplarCyc 1.0: 285 pathways, 3434 enzymes, 1363 compounds (Oct 2009)
Pfam 24.0: 11,912 protein families (Oct 2009)
Poplar Transcription Factors
- DPTF: 2,576 putative TF (March 2007)
- PlnTFDB: 2,901 putative TF (July 2009)
29,365 GO terms (Jan 2010)
Poplar/ Willow QTL
- work in progress
- preliminary dataset available
Only loading referenced publications
~15,000 articles

Unique Knowledge Base for Poplar
Proteins annotated with functional information and publications
Based on Comparative genomics and
Protein familyanalysis
Genes, QTLs enriched withpositionalinformation
Data integration was done in Ondex

Ondex Genomics Layout
Genomic Layout displays chromosomes, genes and QTLs
Chromosomal regions and QTLs can be selected

Ondex Genomics Filter
Genes of interest
Enriched protein annotation network

Phenotypic Information in Literature
HMMer: 650581 – HLH
E-Value: 3.4E-7
Score: 30.0
BLAST 217086 – LAX
E-Value: 8.3E-17
Score: 80.88
BLAST 217086 – BHLH63
E-Value: 8.3E-9
Score: 54.3
PMID:13130077
“LAX and SPA: major regulators of shoot branching in rice.”
Poplar protein 217086 We identified two remote homologs in Rice (LAX) and in Arabidopsis (BHLH63),
as well as one protein domain HLH
The LAX homolog contains evidence to be a major regulator of shoot branching
 Hypothesis generation

Dynamic process
Data analysis
Data Integration
Data Input
Graph of concepts and relations
Biological Databases
Parse
Export
Ontologies & Free Text
Data alignment
Concept mapping
Sequence analysis
Text mining
Experimental Data
Hypothesis
New experiments

Acknowledgements
Newcastle members:
Simon Cockell
James Dewar
Eva Holstein
Katherine James
Philip Lord
David Lydall
Matthew Pocock
JochenWeile
Darren Wilkinson
Anil Wipat
Rothamsted members:
Catherine Canevet
Keywan Hassani-Pak
Stephen Hanley
Matthew Hindle
Angela Karp
Shao Chih Kuo
Artem Lysenko
Chris Rawlings
Mansoor Saqi
Andrea Splendiani
Jan Taubert
Manchester members:
Sophia Ananiadou
Paul Dobson
Paul Fisher
Carole Goble
Gina Levow
Pedro Mendes
Raheel Nawaz
Georgina Moulton
Robert Stevens
David Withers
Katy Wolstencroft
Biological collaborators:
Kim Hammond-Kosack
Martin Urban
DimahHabash
David Wild
Katherine Denby
RoxaneLegaie
Former members:
Jacob Köhler
Rainer Winnenberg
Edinburgh members:
Igor Goryanin
Andrew Millar
Luna De Ferrari

Ondex: Data integration and visualisation

by Biogeeks on Jun 02, 2010

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