Arnaud Belcour’s PhD defense: 21st october 2022 14:00

Arnaud Belcour’s PhD defense on “Combining knowledge-based and sequence comparison approaches to elucidate metabolic functions, from pathways to communities” will take place on Friday 21st october 2022 at 14:00, room Métivier at IRISA.

 

Committee:

  • Delphine Ropers, senior researcher, Inria Grenoble
  • David Vallenet, senior researcher, CEA Genoscope Évry-Courcouronnes
  • Karoline Faust, associate professor, KU Leuven
  • Fabien Jourdan, senior researcher, INRAe Toulouse
  • Cédric Lhoussaine, professor, Univ. Lille
  • Samuel Blanquart, researcher, Inria IRISA Rennes
  • Olivier Dameron, professor, Univ. Rennes1, IRISA Rennes
  • Anne Siegel, senior researcher, CNRS IRISA Rennes

 

Abstract:

Metabolism can be modelled and studied at many levels. The first level is the metabolic pathways, which contain a set of chemical transformations leading to the production of compounds of interest. Alternative metabolic pathways were predicted in an alga using a formalism of the metabolic pathway drift and its implementation with constraint programming. The second level is the organism metabolism which contains hundreds of metabolic pathways. A method has been developed to reconstruct homogeneous metabolic networks from heterogeneous public data. The third level is the metabolism of a group of organisms (or taxon) which can be useful to characterize an organism that has not been clearly identified. To achieve this, a method using knowledge engineering and sequence comparison has been created. Finally, the fourth level is the metabolism of a community and the metabolic interaction in this community. A method has been developed to identify the key species among a community.

Nicolas Guillaudeux’s PhD defense: 16th december 2021 14:00 (UTC+1)

Nicolas Guillaudeux’s PhD defense on “Comparing genes’ structures for predicting coding alternative transcripts in human, mouse and dog” will take place on 16th december 2021 14:00 (UTC+1).

The defense will be broadcasted live on youtube https://youtu.be/7HuFFPMFM9U

Committee:

  • BERARD Sèverine, Maîtresse de conférences, Université de Montpellier (rapportrice)
  • LAINE Elodie, Maîtresse de conférences, Sorbonne Université (rapportrice)
  • DIOT Christian, Directeur de recherche INRAE, Rennes (examinateur)
  • LARTILLOT Nicolas, Directeur de recherche CNRS, Lyon (examinateur)
  • VARRE Jean-Stéphane, Professeur, Université de Lille (examinateur)
  • BELLEANNEE Catherine, Maîtresse de conférences, Université de Rennes 1 (encadrante)
  • BLANQUART Samuel, Chargé de recherche Inria, Rennes (encadrant)
  • DAMERON Olivier, Professeur, Université de Rennes 1 (directeur de thèse)

Keywords: Bioinformatics, comparative genomics, alternative splicing, alternative transcription, orthology, transcript prediction

Abstract:
Living organisms are capable of expressing several alternative transcripts (or RNAs) from a single gene. These transcripts are responsible for the regulatory mechanisms of the organism, some of them are translated into protein. Today, detecting the set of transcripts that can be expressed by a gene is an open problem which many computational methods such as RNA sequencing, spliced sequence alignment methods or comparative genomics methods try to address. This thesis proposes a comparative genomics method to compare the sequence of genes shared by several species. The result is a method for predicting transcripts on a multi-species scale, based on a graph structure. This method was applied to three species (human, mouse, and dog). It allowed to predict a relevant number of transcripts as well as to identify a set of genes that are conserved between the three species and that share both the same exonic structures and the same CDS.

Maël Conan’s PhD defense: 23rd March 2021 14:30 (UTC+1)

Maël Conan’s PhD defense on “Constructing xenobiotic enriched maps of metabolism to predict the role of enzymes in DNA adduct formation” will take place on 23rd March 2021 14:30 (UTC+1).

 

The defense will be broadcasted live on youtube at: https://youtu.be/BJyDIzrgxF4

 

Committee:

  • Cédric LHOUSSAINE , Professeur, Université de Lille (Président du jury)
  • Karine Audouze, Maitresse de conférence, Université de Paris
  • Fabien JOURDAN, Directeur de recherche, INRAE, Toulouse (Rapporteur)
  • Sabine PERES, Maitresse de conférence, Université de Paris Saclay (Rapporteur)
  • Sophie LANGOUËT, Directrice de recherche, INSERM, Rennes (Directrice de thèse)
  • Anne SIEGEL, Directrice de recerche, CNRS, Rennes (Directrice de thèse)

 

Keywords: Metabolism prediction ; HAA ; DNA reactivity ; Bayesian Networks

 

Abstract:

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food addi- tives, etc.). Among environmental contaminants of concern, heterocyclic aromatic amines (HAAs) are xenobiotics classified as possible or probable carcinogens (2A or 2B) by IARC, for which low information exists in humans. 30 AHAs have been identified to date, but the bioactivation pathways, metabolites and DNA adducts have been fully characterised in the human liver for only three of them (MeIQx, PhIP, AαC). We have developed a modelling approach to predict both metabolism (metabolites and reactions), DNA reactivity and the production probability of metabolite. Our approach is based on the construction of enriched metabolism maps. We bring together tools for predicting reactions and metabolites (SyGMa), pre- dicting metabolism sites (Way2Drug SOMP, Fame3), predicting DNA reactivity (XenoSite Reactivity V1) and calculating a production probability score based on the properties of Bayesian networks. This prediction pipeline was evaluated and validated using caffeine and then applied to six AHAs. Main results show that our approach allows us to predict the metabolism of xenobiotics and that the production probability score has different proper- ties that can lead to the filtration of the metabolism map or to the determination of the enzymatic pro- files associated with maximising the formation of DNA adducts. This predictive toxicology approach opens up prospects for estimating the genotoxicity of various environmental contaminants in normal or pathophysiological situations.

Hugo Talibart’s PhD defense: 24th February 2021 14:30 (UTC+1)

Hugo Talibart’s defense on “Comparison of homologous protein sequences using direct coupling information by pairwise Potts model alignments” will take place on Wednesday 24th February 2021 at 14:30 (UTC+1).

 

The defense will be broadcasted live on youtube at: https://youtu.be/NtBNWGjifv8

 

Committe:

  • Sean EDDY, Professor at Harvard University, Cambridge, USA (Rapporteur)
  • Martin WEIGT, Professor at Sorbonne Université, Paris (Rapporteur)
  • Guillaume GRAVIER, Senior researcher CNRS, Rennes
  • Juliette MARTIN, Researcher CNRS, Lyon
  • Thomas SCHIEX, Senior researcher INRAE, Toulouse
  • Jacques NICOLAS, Senior researcher Inria, Rennes (thesis Director)
  • François COSTE, Researcher Inria, Rennes (thesis supervizor)

 

Abstract:

To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods based on significant alignments of query sequences to annotated proteins or protein families. While powerful, existing approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, in this thesis we propose to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition, and to compare proteins by aligning these models. This novel application of Potts models raised further requirements for their construction, and we identified several key points towards building more comparable Potts models, towards an ideal of canonicity. Due to non-local dependencies, the problem of aligning Potts models is NP-hard. Here, we introduced a method based on an Integer Linear Programming formulation of the problem which can be optimally solved in tractable time. Our first results suggest that taking pairwise couplings into account can improve the alignment of remote homologs and could thus improve remote homology detection.

Méline Wéry’s PhD defense: 16th december 14:00

Méline Wéry’s PhD defense on “Identification of causal pathologic signature by multi-omic data integration” will take place on Thursday 16th december at 14:00 (UTC+1).

The defense will be broadcasted live on youtube at: https://youtu.be/5aL92INe2NI

Committee:

– Emmanuelle BECKER (Université de Rennes 1)
– Charles BETTEMBOURG (Sanofi, Chilly-Mazarin)
– Laurence CALZONE (Institut Curie, Paris)
– Olivier DAMERON (Université de Rennes 1)
– Franck DELAPLACE (Université de Paris-Saclay, Évry)
– Fleur MOUGIN (LaBRI Bordeaux)
– Anne SIEGEL (IRISA Rennes)
– Vassili SOUMELIS (Hôpital Saint Louis, Paris)
– Emmanuel OGER (Université de Rennes 1)

 

Abstract:

Systematic erythematosus lupus is an example of a complex, heterogeneous and multifactorial disease. The identification of signature that can explain the cause of a disease remains an important challenge for the stratification of patients. Classic statistical analysis can hardly be applied when population of interest are heterogeneous and they do not highlight the cause. This thesis presents two methods that answer those issues. First, a transomic model is described in order to structure all the omic data, using semantic Web (RDF). Its supplying is based on a patient-centric approach. SPARQL query interrogates this model and allow the identification of expression Individually-Consistent Trait Loci (eICTLs). It a reasoning association between a SNP and a gene whose the presence of the SNP impact the variation of its gene expression. Those elements provide a reduction of omics data dimension and show a more informative contribution than genomic data. This first method are omics data-driven. Then, the second method is based on the existing regulation dependancies in biological networks. By combining the dynamic of biological system with the formal concept analysis, the generated stable states are automatically classified. This classification enables the enrichment of biological signature, which caracterised a phenotype. Moreover, new hybrid phenotype is identified.

 

Marine Louarn PhD’s defense : 26th november 09:30

Marine Louarn’s PhD defense on “Analysis and integration of heterogeneous large-scale genomics data: application to B cell differentiation and Follicular Lymphoma non coding mutations” will take place on Thursday 26th november at 09:30 (CEST+1).

The defense will be broadcasted live on Youtube: https://youtu.be/9kPjm6yERMM

Committee

– Adrien COULET (MCU) LORIA, Nancy

– Lydie LANE (Researcher), SIB Lausanne, Swiss

– Salvatore SPICUGLIA (DR) INSERM U1090 Marseilles

– Alexandre TERMIER (Professor) University Rennes 1 Rennes

– Sarah COHEN-BOULAKIA (Professor) LRI Orsay

– Fabrice CHATONNET (IR) CHU Rennes

– Olivier DAMERON (Professor) University Rennes 1 Rennes

– Anne SIEGEL (DR CNRS) IRISA Rennes

– Thierry FEST (PU-PH) INSERM / CHU Rennes

 

Abstract:
Regulatory networks inference from heterogeneous data is a computational step aiming at identifying key regulators involved in differentiation processes leading to cancer. In this thesis I focus on B cell differentiation, from which follicular lymphoma emerges. The first contribution outlines the reproducibility and reusability limitations of a state-of-the-art method for network inference from genomic data. To overcome these limitations, I demonstrated that Semantic Web technologies can structure and integrate large-scale heterogeneous datasets in a systematic way. The original analysis workflow outputs could be reproduced as queries on a graph of data, which could itself be layered and enriched with public databases. This demonstrates the technical relevance of this approach and underlines its benefits in improving reusability and reproducibility. As a fourth contribution, a new method for network inference was designed to take expert knowledge into account – both to extend the previous framework to the analysis of smaller, closely-related datasets and to enrich the inferred networks with signs, therefore including inhibitory regulatory processes. Finally, the method was applied to B cell differentiation, leading to the discovery of 146 TF with potential large impact on the network.

Corentin Raphalen

Corentin Raphalen

Research Engineer, CNRS


Formation



 

Dyliss team, Irisa / Inria Rennes-Bretagne Atlantique,
Email: corentin.raphalen@irisa.fr
ORCID: 0000-0002-1322-7007