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

BRANE Clust: cluster-assisted gene regulatory network inference refinement

The joined Gene Regulatory Network (GRN)  inference and clustering tool BRANE Clust has just been published in BRANE Clust: cluster-assisted gene regulatory network inference refinement in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017 (doi:10.1109/TCBB.2017.2688355).

It is also featured on RNA-seq blog and OMIC tools.

Alternative versions are available as a preprint, on biorxiv, with a page and software and in HAL. Another brick in the BRANE series wall, a series of bioinformatics tools based on graphs and optimization, dedicated to -omics gene expression data for GRN (Gene Regulatory Network) inference.

While traditional Next-generation sequencing (NGS) pipelines often combine motley assumptions (correlation, normalization, clustering, inference), this work is an first step toward gracefully combining network inference and clustering. 

BRANE Clust works as a post-processing tool upon classical network thresholding refinement. From a complete weighted network (obtained from any network inference method) BRANE Clust favors edges both having higher weights (as in standard thresholding) and linking nodes belonging to a same cluster. It  relies on an optimization procedure. It  computes an optimal gene clustering (random walker algorithm) and an optimal edge selection jointly. The introduction of a clustering step in the edge selection process improves gene regulatory network inference. This is demonstrated on both synthetic (five networks of  DREAM4 and network 1 of DREAM5) and real (network 3 of DREAM5) data. These conclusions are drawn after comparing classical thresholding on CLR and GENIE3 networks to our proposed post-processing. Significant improvements in terms of Area Under Precision-Recall curve are obtained. The  predictive power on real data yields promising results: predicted links specific to BRANE Clust reveal plausible biological interpretation. GRN approaches that produce a complete weighted network to prune could benefit from BRANE Clust post-processing.

Escherichia coli network built using BRANE Clust on GENIE3 weights and containing 236 edges. Large dark gray nodes refers to transcription factors (TFs). Inferred edges also reported in the ground truth are colored in black while predictive edges are light gray. Dashed edges correspond to a link inferred by both BRANE Clust and GENIE3 while solid links refer to edges specifically inferred by BRANE Clust.

Abstract:

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells.
Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e. CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at:
http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-clust.html