This talk will focus mainly on, ongoing projects at the Institute of Computational Biology Agronomic Linked Data (AgroLD): is a Semantic Web knowledge base designed to integrate data from various publically available plant centric data sources. GIGwA: is a tool developed to manage genomic, transcriptomic and genotyping large data resulting from NGS analyses.

1. Enabling
knowledge
management
in
the
Agronomic
Domain
Pierre
Larmande
Ins-tute
of
Computa-onnal
Biology
(IBC)
Pierre.larmande@ird.fr

2. Mul7-­‐scale
omics
integra7on
BIG
DATA
Research
areas
Workflow
management
Seman7c
web

3. Outline
• Data integration challenges in the Life Sciences"
• Ontologies/ Semantic Web Technologies"
• Agronomic Linked Data project"
• NGS Big Data project"

4. Data landscape in the Life Sciences
• The availability of biological data has increased"
• Advancements in:"
• computational biology"
• genome sequencing"
• high-throughput technologies "
• Integrative approaches are necessary to understand
the functioning of biological systems"

5. • Lack of effective approaches to integrate data that
has created a gap between data and knowledge"
• Need for an effective method to bridge gap between
data and underlying meaning"
• Harvest the power of overlaying different data sets"
Data integration challenges

6. • Today’s Web content is suitable for human consumption"
• Collection of documents"
• the existence of links that establish connections
between documents"
• Low on data interoperability and lacks semantics."
Today’s Web

7. • Ontologies are formal representations of knowledge - definitions of
concepts, their attributes and relations between them."
• To integrate data, improve machine interoperability and data
analysis required a conceptual scaffold."
• Ontological terms used across databases"
• provide cross-domain common entry points in the description."
• An array of ontologies are being used to bring structured integration
of various datasets."
• The Open Biomedical Ontologies (OBO) initiative:"
• serves as an umbrella for well structured orthogonal
ontologies."
• Ontologies represented in OBO format and OWL"
Ontologies

8. Semantic Web Technology
• An extension of the current Web technologies."
• Enables navigation and meaningful use of digital
resources."
• Support aggregation and integration of information
from diverse sources."
• Based on common and standard formats."

9. Resource Description Framework (RDF)
• Framework for representing information about resources
on the Web"
• Provides a labeled connection between two resources"
• Uses Unique Resource Identifiers (URI)"
• Statements take the form of triples:"
Subject
Predicate
Object
<Gene_A>
<codes_for>
<Protein_A>
RDF
Triple

10. • Combining the triples results in a directed, labeled
graph."
<Gene_A>
<Protein_A>
<has_func7on>
<BP_A>
<MF_A>
<Gene_X>
<regulates>

11. SPARQL
• Language which allows querying RDF models (graphs) "
• Powerful, flexible"
• Its syntax is similar to the one of SQL"

12. Agronomic Linked Data (AgroLD)
• Semantic web based system that captures knowledge
pertaining to plant data"
• Aim:"
• Capability to answer complex real life questions"
• Efficient information integration / retrieval"
• Easy extensibility"

13. AgroLD – Phase I
• AgroLD will be developed in phases – "
• Phase I: includes data on Oryza sps. and Arabidopsis thaliana!
• SPARQL endpoint: http://bioportal.lirmm.fr:8081/test/ "
AgroLD

14. • Integrates information from:"
• Ontologies: Gene Ontology, Plant Ontology, Plant Trait Ontology,
Plant Environment Ontology, Crop Ontology"
• Other information sources: "
• Gene/Protein information: GOA, Gramene, Tair, OryzaBase,
UniPort "
• QTL information: Gramene"
• Pathway information: RiceCyc, AraCyc"
• Germplasm information: Oryza Tag Line"
• Homology prediction: GreenPhyl"
Information in AgroLD

15. Biological
process
Cellular
Component
Molecular
Function
Interaction
Gene
Taxon
Protein
protein
Modification
Pathway
has_par7cipant
contains
has_func7on
has_agent
acts_on
is_member_of
codes_for
occurs_in
has_source
protein
Target
Gene
Knowledge representation in AgroLD

16. Future directions
• Phase II: having both wider and deeper coverage to promote
comparative analysis"
• Include varied data types – gene expression data, protein-protein
interaction, Transcription factor- target gene "
• Developing methods to aid the process of hypotheses generation - e.g.
inference rules."
• Pluggable with workflow platforms e.g.: Galaxy, OpenAlea
(VirtualPlants)."
• Engage with biologists to mobilise ‘user-pull’:"
• Develop real world use cases – studying the molecular mechanism
of panicle differentiation in rice"

17. Gigwa:
Genotype
Inves7gator
for
Genome
Wide
Analyses

18. Aims
of
Gigwa
• Manage
genomic,
transcriptomic
and
genotyping
data
resul-ng
from
NGS
analyses
• Handle
large
VCF
files
to
filter,
query
and
extract
data
• Provide
a
web
user
interface
to
make
the
system
accessible
for
all

19. Main
features
o Based
on
NoSQL
technology
o Handles
VCF
files
(Variant
Call
Format)
and
annota-ons
o Supports
mul-ple
variant
types:
SNPs,
InDels,
SSRs,
SV
o Powerful
genotyping
queries
o Easily
scalable
with
MongoDB
sharding
o Transparent
access

20. Demo
–
Database
selec7on
Mul--­‐database
with
restricted
access

21. Demo
–
datatypes
selec7on
Several types of selection:
Variant search is restricted to the
selected reference sequences
and subset of individuals

22. Demo
-­‐
Queries
Predefined queries

23. Demo
–
Filters
selec7on
Several
filters

24. Demo
–
browsing
results

25. Acknowledgements
Dr. Patrick Valduriez!
Dr. Clement Jonquet
Dr. Manuel Ruiz! Guilhem Sempéré!
Alexis DereeperGautier Sarah
Dr. Aravind Venkatesan
Florian Philippe
Contact:
pierre.larmande@ird.fr