[SocBiN] Postdoc position - Knowledge models for analysis and interpretation of genetic data - Paris (France)

[Olivier Colliot]

Dear colleagues,

We have a postdoc position on Knowledge models for analysis and
interpretation of genetic data.
The profile is available at: http://aramislab.prod.lamp.cnrs.fr/wp-content/
uploads/2017/02/Post-doc-2017_IPL_Neuromarkers_For_diffusion-2.pdf
Could you please help us circulate this announce?

Best regards
Olivier Colliot

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JOB OFFER - Postdoctoral fellow - Knowledge models for analysis and
interpretation of genetic data in neurodegenerative diseases
Keywords: computational biology, bioinformatics, knowledge models,
ontologies, genomic data

LABORATORY: Brain and Spinal Cord Institute (ICM), Paris, France

PROJECT
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 (www.geneontology.org) for describing
gene products, Reactome (www.reactome.org) 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 (https://github.com/askomics/askomics) 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.

YOUR PROFILE
PhD in Computer Science, Bioinformatics, Computational Biology or a related
field
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.
Strong relational skills to interact with professionals from various
backgrounds.
Ability to synthesize informations from different sources
Excellent written and oral communication skills

Starting date: Around November 2017
Duration: 18 months

CONTACTS
Olivier Colliot - Olivier.Colliot at upmc.fr
Ivan Moszer – i.moszer-ihu at icm-institute.org
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