=Paper= {{Paper |id=None |storemode=property |title=Using Semantic Web Technology to Accelerate Plant Breeding |pdfUrl=https://ceur-ws.org/Vol-952/paper_41.pdf |volume=Vol-952 |dblpUrl=https://dblp.org/rec/conf/swat4ls/ChibonCWVF12 }} ==Using Semantic Web Technology to Accelerate Plant Breeding== https://ceur-ws.org/Vol-952/paper_41.pdf
      Using semantic web technology to accelerate
                    plant breeding.

    Pierre-Yves Chibon1,2,3 , Benoı̂t Carrères1 , Heleena de Weerd1 , Richard G. F.
                         Visser1,2,3 , and Richard Finkers1,3
1
     Wageningen UR Plant Breeding, Wageningen University and Research Centre, 6708
                          PB Wageningen, The Netherlands.
    2
      Experimental Plant Sciences, Wageningen University and Research Centre, 6708
                           PB Wageningen, The Netherlands
      3
        Centre for BioSystems Genomics, Wageningen, 6708 PB, Wageningen, The
                                     Netherlands



         Abstract. One goal within plant breeding is to find the causal gene(s)
         explaining a given phenotype. Semantic web technology brings opportu-
         nities to integration data and information accross spread data sources.
         Chebi2gene and Marker2sequence are two applications relying on this se-
         mantic web technology to integration genes, proteins, metabolites, path-
         ways, literature. Their web-based interface allows biologists to use and
         explore this network of information.

         Keywords: Semantic web applications, Data integration, Plant Breed-
         ing


1      Introduction
”Producing 70 percent more food for an additional 2.3 billion people by 2050
while at the same time combating poverty and hunger, using scarce natural re-
sources more efficiently and adapting to climate change are the main challenges
world agriculture will face in the coming decades” (http://www.fao.org/news/story/en/item/35571/).
Plant breeding is part of the answer to this challenge. The FAO itself recognize
it: ”Plant breeding techniques can lead to improved crop varieties that increase
yields, decrease losses” (http://www.fao.org/news/story/en/item/35686/). In
order to improve crop varieties, breeders introgress genes of interest from one ac-
cession to another. The challenge is to pinpoint the gene(s) responsible for the im-
proved traits. To find these genes, plant breeders use all the new types of informa-
tion, which become available using high-throughput technologies, such as next-
generation sequencing technology, RNASeq, proteomics and/or metabolomics.
    As a daily practice, plant breeders associate these large datasets to one or
several regions of the genome using advanced statistical methodology. These re-
gions are called Quantitative Trait Loci (QTLs) and can be introgressed from
one variety to another with the goal to developed an improved variety. However,
a typical QTL region may contain over hundreds of genes, including genes nega-
tively influencing the breeding goals. Complete genome sequences of many crop
plants are becoming available, including the genome of important food crops
such as tomato [1] and potato [2]. The availability of structural and functional
genome annotations makes it possible to investigate the QTL region for genes
positively or negatively influencing the trait of interest.
    Plant breeding, as most research areas nowadays, faces the problem of spread-
ing data resources. Most plant species have their own website, ideally cross-
referenced to major cross-species database such as UniProt or GO, but the
number of resources available keeps increasing every year. When a researcher
starts to investigate the genes in a specific QTL region on a genome, he will
have to browse through an increasing number of websites and databases to col-
lect and integrate information about each of these genes. One solution to this
problem is to use semantic web technologies to aggregate and integrate the data
from different resources in a way that would be and automated and expandable
to new resources as they become available.
    Within Wageningen UR Plant Breeding, we have developed two new tools
relying on semantic web technologies to help breeders face this challenge, namely:
Chebi2gene and Marker2sequence.


2   Materials and Methods
Our tools have been primary developed for usage with the Tomato genome se-
quence, however, they should are implemented in a universal manner. The lim-
iting factor is that the annotation of a genome sequence should be available
in RDF. To convert the Tomato genome annotation into RDF we developed
a simple tool: gff2RDF, which retrieves and parses the gff file and outputs a
RDF document of the annotation. This tool is used for Tomato and is being
extended to work with Potato and Arabidopsis thaliana. In the conversion the
gene annotation linked to external database have been converted to use the URI
of these database, i.e.: the GO terms associated with the genes are identified
using the URI provided by the OWL file of the Gene Ontology consortium, and
similarly for the protein identifier against UniProt. The resulting RDF file has
been uploaded into a Virtuoso OSE server (v 6.1.3) together with the RDF files
provided by EBI for UniProt-core, UniProt-pathways, UniProt-go and UniProt-
citations (all version 2011 10), CHEBI (version 2011 09), Rhea (release 33), the
Gene Ontology OWL file as provided by the Gene Ontology Consortium (version
2011 11 03). Each resource is stored in its own graphs (Fig 1), allowing easier
upgrades, and SPARQL is used to do the integration.


3   Chebi2gene, linking metabolites to genes
Metabolites are chemical compounds produced by an organism, in plants their
actions can be related to traits of major interest such as flavor or disease resis-
tance. The link between a metabolite and the genes involved in its expression is
not always straightforward. Chebi2gene is a proof of concept allowing breeders
to go from one metabolite to the associated gene(s). This allows biologists to
Fig. 1. This figure represents the integration of the different resources in our triple
store. The green box defines the different RDF graphs and the red ellipse the type of
information we extract from these graphs. The dashed gray line between around Rhea
is for the fact that Rhea do not reuse the URI of Chebi or UniProt when refering to
Chebi compound or a UniProt protein. The mapping is then indirect, as opposed to
the plain gray line where the URI are consistent and shared.



find all the genes in the genome related to a metabolite. To find these associa-
tions, it uses CHEBI, Rhea, and UniProt databases and our RDF version of the
tomato genome annotation. The input is either a CHEBI identifier or the name
of the metabolite. If the input is a name, Chebi2gene will search the CHEBI
database for all compounds having this name in their name and optionally in
their synonyms. Once the metabolite has been uniquely identified with a CHEBI
identifier, Chebi2gene search in Rhea all the chemical reactions in which it is in-
volved, then all the proteins which are involved in these reactions and finally all
the genes from the genome annotation which are related to these proteins. These
searches are performed using SPARQL queries on our Virtuoso server across the
different graphs of the different resources.

   Chebi2gene is available at: http://www.plantbreeding.wur.nl/chebi2gene

   For example, when searching for ”beta-carotene” in chebi2gene, three molecules
containing ”beta-carotene” are returned: ”beta-carotene”, ”beta-carotene 5,6-
epoxide”, and ”(5S,6R)-beta-carotene 5,6-epoxide”. From these three molecules,
the first one is the molecule of interest. It has the CHEBI identifier 17579.
Searching with this identifier in chebi2gene, we can find that this compound is
involved in 4 reactions which are associated with 10 proteins. Amongs these
proteins is ”Lycopene beta cyclase” (UniProt ID: Q38933) which is associ-
ated with two pathways: ”Carotenoid biosynthesis; beta-carotene biosynthesis”
and ”Carotenoid biosynthesis; beta-zeacarotene biosynthesis” and four genes:
Solyc04g040190.1.1 (chromosome 4) and Solyc10g079480.1.1 (chromosome 10)
which are both ”Beta-lycopene cyclase” and Solyc06g074240.1.1 (chromosome
6) and Solyc12g008980.1.1 (chromosome 12) which are both ”Lycopene beta
cyclase”.


4    Marker2sequence explore a genome region for a
     candidate gene

Marker2sequence [3] aims at mining quantitative trait loci (QTLs) for candidate
genes. For each gene, within the QTL region, marker2sequence uses semantic
web integration technology to integrate putative gene function with associated
Gene Ontology terms, proteins, pathways and literature. This integration is per-
formed using SPARQL queries against our triple-store. As mention earlier, a
typical QTL region easily contains several hundreds of genes, this gene list can
then be further filtered using a keyword based query on the aggregated anno-
tations. This single query search for the given keyword in the gene annotation
and GO terms, proteins and literature associated with this gene. More precisely,
it searches the keyword in he name and definition and synonym of the Gene
Ontology term associated with the gene. It searches the keyword in the protein
name and description of each protein associated with the gene. It searches the
keyword in the pathway name of each pathways associated to these proteins and
finally it searches in all title and abstract of the literature associated with these
proteins. If any of these elements contains the searched keyword the gene is se-
lected as a potentially interesting gene and returned to the user Marker2sequence
will help breeders to identify potential candidate genes for their traits of interest.

    Marker2sequence is available at: http://www.plantbreeding.wur.nl/BreeDB/marker2seq

    For example, β-carotene content is a trait influencing the color of tomatoes
[4]. Based on our QTL analysis, using data from the Solanum lycopersicum x
Solanum galapagense LA0483 RIL population [5], this compound has one QTL
on chromosome 6 (between TG253 and TG314). Marker2sequence identifies 988
genes in this region of the chromosome 6. A query with the keyword: beta-
carotene, returns the gene Solyc06g074240.1.1. This gene, Solyc06g074240.1.1, is
associated with the GO term for carotenoid biosynthetic process, the pathway for
Carotenoid biosynthesis and more specifically the part of beta-carotene biosyn-
thesis. Information for each gene can be quickly mined using Marker2sequence
and this gene is the candidate for our trait of interest.


5    Conclusion

Both Chebi2gene and Marker2sequences are tools presenting the potential of
semantic web integration for the plant breeding domain.
   All the tools presented in this abstract have been licensed under free license
and are available at: http://github.com/PBR/
References
[1] The Tomato Genome Consortium. The tomato genome sequence provides insights
  into fleshy fruit evolution. Nature 485 (2012) 635641.
[2] The Potato Genome Sequencing Consortium. Genome sequence and analysis of the
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[3] Chibon, P-Y., Schoof, H., Visser, R.G.F., Finkers, R.: Marker2sequence, mine your
  QTL regions for candidate genes. Bioinformatics 28 (2012) 1921-1922.
[4] Lincoln, R.E., Porter, J.W.: Inheritance of beta-carotene in tomatoes. Genetics 35
  (1949) 206-211.
[5] Paran, I. Goldman, I., Tanksley, S.D., Zamir, D.: Recombinant inbred lines for
  genetic mapping in tomato. Theor. Appl. Genet. 90 (1995) 542-548.