Bioinformatics & datascience: Internship & PhD on multi-omics data

An PhD position is still available on Graph-based learning from integrated multi-omics and multi-species data (genomic, transcriptomic, epigenetic) between IFP Energies nouvelles and CentraleSupélec/INRIA Saclay. All the information is gathered at this address.

Some information is duplicated below:

• Application for the funded PhD/doctorate (IFP Energies nouvelles/CentraleSupélec) position on Graph-based learning from integrated multi-omics and multi-species data (genomic, transcriptomic, epigenetic), starting Sep.-Dec. 2018
• Excellent applications will be evaluated on a first come/first served basis
• Material: send resume/CV with grades, and a short memo/letter explaining your interest and skill-matchfor this topic, to fragkiskos(dod)malliaros(ad)centralesupelec(dod)fr, laurent(dod)duval(ad)ifpen(dod)fr and aurelie(dod)pirayre(ad)ifpen(dod)fr
• PhD advisor: Jean-Christophe Pesquet, Center for Visual Computing (or CVN, Centre de vision numérique), CentraleSupélec, INRIA Saclay
• Supervising team (a superset for both the internship and the PhD):
  • Frédérique Bidard-Michelot (IFP Energies nouvelles, Biotechnologies)
  • Laurent Duval (IFP Energies nouvelles)
  • Fragkiskos D. Malliaros (CentraleSupelec)
  • Aurélie Pirayre (IFP Energies nouvelles)
  • Jean-Christophe Pesquet (CentraleSupelec)
• PDF description Bioinformatics graph processing for multi-omics networks

Micro-organisms are studied here for their application to bio-based chemistry from renewable sources. Such organisms are driven by their genome expression, with very diverse mechanisms acting at various biological scales, sensitive to external conditions (nutrients, environment). The irruption of novel high-throughput experimental technologies provides complementary omics data and, therefore, a better capability for understanding for the studied biological systems. Innovative analysis methods are required for such highly integrated data. Their handling increasingly require advanced bioinformatics, data science and optimization tools to provide insights into the multi-level regulation mechanisms (Editorial: Multi-omic data integration). The main objective of this subject is to offer an improved understanding of the different regulation levels in the cell (from model organisms to Trichoderma reesei strains). The underlying prediction task requires the normalization and the integration of heterogeneous biological data (genomic, transcriptomic and epigenetic) from different microorganisms. The path chosen is that of graph modelling and network optimization techniques, allowing the combination of different natures of data, with the incorporation of biological a priori (in the line of BRANE Cut and BRANE Clust algorithms). Learning models relating genomic and transcriptomic data to epigenomic traits could be associated to network inference, source separation and clustering techniques to achieve this aim. The methodology would inherit from a wealth of techniques developed over graphs for scattered data, social networks. Attention will also be paid to novel evaluation metrics, as their standardization remains a crucial stake in bioinformatics. A preliminary internship position (summer/fall 2018) is suggested before engaging the PhD program. Information at: http://www.laurent-duval.eu/lcd-2018-intern-phd-epigenetics-omics-graph-processing.html

• BRANE Power page
• A. Pirayre, C. Couprie, L. Duval, F. Bidard, J.-C. Pesquet, BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference, 2015, BMC Bioinformatics
• A. Pirayre, C. Couprie, L. Duval, J.-C. Pesquet, BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement, 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics
• BRANE Cut software page
• BRANE Clust software page
• Enhancement with Graph cuts for Gene Regulatory Network inference, BMC Bioinformatics D. Seux, F. D. Malliaros, A. Papadopoulos, M. Vazirgiannis, 2017, Core Decomposition of Uncertain Graphs Using Representative Instances, International Conference on Complex Networks and Their Applications