<div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr">Dear colleagues,<div><br></div><div>We have a postdoc position on Knowledge models for analysis and interpretation of genetic data.</div><div>The profile is available at: <a href="http://aramislab.prod.lamp.cnrs.fr/wp-content/uploads/2017/02/Post-doc-2017_IPL_Neuromarkers_For_diffusion-2.pdf" target="_blank">http://aramislab.prod.<wbr>lamp.cnrs.fr/wp-content/<wbr>uploads/2017/02/Post-doc-2017_<wbr>IPL_Neuromarkers_For_<wbr>diffusion-2.pdf</a> </div><div>Could you please help us circulate this announce?</div><div><br></div><div>Best regards</div><div>Olivier Colliot</div><div><br clear="all"><div><div class="m_-4252140592457199493gmail-m_-3078185850053185318gmail_signature"><div dir="ltr"><div><div dir="ltr"><div>--------------</div></div></div></div></div></div></div></div></div><div><br></div><div>JOB OFFER - Postdoctoral fellow - Knowledge models for analysis and interpretation of genetic data in
neurodegenerative diseases </div><div>Keywords: computational biology, bioinformatics, knowledge models, ontologies, genomic
data </div><div><br></div><div>LABORATORY: Brain and Spinal Cord Institute (ICM), Paris, France</div><div><br></div><div>PROJECT</div><div>Neurodegenerative diseases (such as Alzheimer’s disease and Parkinson’s disease) are major
public health concerns. To develop new treatments for these diseases, it is crucial to identify
at the earliest stage (ideally presymptomatic) the patients that will develop the disease.
Genetic factors play an important role in these diseases. A major goal is to identify genetic
variants and their combination that can influence disease evolution. To that aim, knowledge
models of biological processes at play appear essential. First, such knowledge models could
be used to inform the analysis of genetic variants (identified through sequencing and microarray
technologies), for instance by constraining statistical learning approaches. These
models are also essential for the biological interpretation of the discovered variants.
The objective of this post-doctoral project is to design approaches to integrate
knowledge models of biological processes in neurodegenerative diseases in the analysis of
genetic variants. These will include both healthy and pathological metabolic and signaling
pathway models. Pathways models can formalize the relationships between different gene
activations in a given biological process or cellular cycle. The building of such models and
their use with patient-specific data relies on approaches from the domains of ontologies,
semantic web and graph-based representations. Different knowledge bases, such as that of
the Gene Ontology (<a href="http://www.geneontology.org" target="_blank">www.geneontology.org</a>) for describing gene products, Reactome
(<a href="http://www.reactome.org" target="_blank">www.reactome.org</a>) for describing pathways, or OMIM and the Disease Ontology for
describing pathologies have been developed by the scientific community. However, many of
these models are either relatively generic or developed for other types of diseases (mainly
cancer). Specific models of neurodegenerative disease have been proposed but the tools to
automatically use these models for analysis of genetic data are still underdeveloped.
Furthermore, knowledge about regional effects (such as effect on specific brain structures)
needs to be added for better integration with imaging data. The present project will thus aim
to propose knowledge models which are better adapted to these pathologies. These
knowledge models will be based upon the increasing interoperability between specialized
data repositories enabled by the Linked Open Data Initiative. Another important element is
the ability to create a mapping between the knowledge model and the genetic data to be
analyzed (such as for instance sets of Single Nucleotide Polymorphisms or structural
variants). Such a mapping is non-trivial, in particular in non-coding regions and because of
distant regulations. The second aim of the project will thus be to develop mapping strategies
that can map knowledge models to genetic data. To address both issues, we propose to use
query building tools such as the Askomics (<a href="https://github.com/askomics/askomics" target="_blank">https://github.com/askomics/<wbr>askomics</a>) tool in
development by Dyliss. Askomics supports both the integration of tabulated data into an
RDF triplestore, and an intuitive interface for generating SPARQL queries in order to analyze
them in combination with domain ontologies. Based on this approach, the first step of the project will be to integrate and standardize all genomic data produced in the project, and to
link these datasets with external disease and pathway databases. The next step will be to
extract for the local RDF database suitable gene-dependencies networks that will be used as
a-priori knowledge for statistical methods. As a final step, the post-doc will represent the
mapping between variants and regulated genes by taking into account additional genomic
information. </div><div><br></div><div>YOUR PROFILE </div><div>PhD in Computer Science, Bioinformatics, Computational Biology or a related field</div><div>Previous work on ontologies or semantic web technologies for genomic data would be a
plus. Alternatively, an expertise in genomic sequence analysis (SNP, variants) would be
highly appreciated.</div><div>Strong relational skills to interact with professionals from various backgrounds.</div><div>Ability to synthesize informations from different sources </div><div>Excellent written and oral communication skills </div><div><br></div><div>Starting date: Around November 2017 </div><div>Duration: 18 months </div><div><br></div><div>CONTACTS</div><div>Olivier Colliot - <a href="mailto:Olivier.Colliot@upmc.fr" target="_blank">Olivier.Colliot@upmc.fr</a> </div><div>Ivan Moszer – <a href="mailto:i.moszer-ihu@icm-institute.org" target="_blank">i.moszer-ihu@icm-institute.org</a><br></div></div>
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