3.2.1 Research topics

Facilitating data integration and querying The quantity and inner complexity of life science data require semantically-rich analysis methods. A major challenge is then to combine data (from local project as well as from reference databases) and symbolic knowledge seamlessly. Semantic Web technologies (RDF for annotating data, OWL for representing symbolic knowledge, and SPARQL for querying) provide a relevant framework, as demonstrated by the success of Linked (Open) Data  34. However, life science end users (1) find it difficult to learn the languages for representing and querying Semantic Web data, and consequently (2) miss the possibility they had to interact with their tabulated data (even when doing so was exceedingly slow and tedious). Our first objective in this axis is to develop accurate abstractions of datasets or knowledge repositories to facilitate their exploration with RDF-based technologies.

Scalability of semantic web queries. A bottleneck in data querying is given by the performance of federated SPARQL queries, which must be improved by several orders of magnitude to allow current massive data to be analyzed. In this direction, our research program focuses on the combination of linked data fragments  56, query properties and dataset structure for decomposing federated SPARQL queries.

Building and compressing static maps of interacting compounds A final approach to handle heterogeneity is to gather multi-scale data knowledge into functional static map of biological models that can be analyzed and/or compressed. This requires to linking genomics, metabolomics, expression data and protein measurement of several phenotypes into unified frameworks. In this direction, our main goal is to develop families of constraints, inspired by symbolic dynamical systems, to link datasets together. We currently focus on health (personalized medicine) and environmental (role of non-coding regulations, graph compression) datasets.

3.2.2 Associated software tools

AskOmics platform AskOmics is an integration and interrogation software for linked biological data based on semantic web technologies1. AskOmics aims at bridging the gap between end user data and the Linked (Open) Data cloud (LOD cloud). It allows heterogeneous bioinformatics data (formatted as tabular files or directly in RDF) to be loaded into a Triple Store system using a user-friendly web interface. It helps end users to (1) take advantage of the information readily available in the LOD cloud for analyzing there own data and (2) contribute back to the linked data by representing their data and the associated metadata in the proper format as well as by linking them to other resources. An originality is the graphical interface that allows any dataset to be integrated in a local RDF datawarehouse and SPARQL query to be built transparently and iteratively by a non-expert user.

FinGoc-tools The FinGoc tools allow filtering interaction networks with graph-based optimization criteria in order to elucidate the main regulators of an observed phenotype. The main added-value of these tools is to make explicit the criteria used to highlight the role of the main regulators.

(1) The KeyRegulatorFinder package searches key regulators of lists of molecules (like metabolites, enzymes or genes) by taking advantage of knowledge databases in cell metabolism and signaling2. (2) The PowerGrasp python package implements graph compression methods oriented toward visualization, and based on power graph analysis3. (3) The iggy package enables the repairing of an interaction graph with respect to expression data4.

3.3.1 Research topics

Genomic level: characterizing functions of protein sequences Precise characterization of functional proteins, such as enzymes or transporters, is a key to better understand and predict the actors involved in a metabolic process. In order to improve the precision of functional annotations, we develop machine learning approaches taking a sample of functional sequences as input to infer a grammar representing their key syntactical characteristics, including dependencies between residues. Our first goal is to enable an automatic semi-supervised refinement of enzymes classification 6 by combining the Protomata-Learner 40 framework - which captures local dependencies - with formal concept analysis. More challenging, we are exploring the learn of grammars representing long-distance dependencies such as those exhibited by contacts of amino-acids that are far in the sequence but close in the 3D protein folding.

System level: enriching and comparing metabolic networks for non-model organisms

Non-model organisms are associated with often incomplete and poorly annotated sequences, leading to draft networks of their metabolism which largely suffer from incompleteness. In former studies, the team has developed several methods to improve the quality of eukaryotes metabolic networks, by solving several variants of the so-called Metabolic Network gap-filling problem with logical programming approaches 10, 9. The main drawback of these approaches is that they cannot scale to the reconstruction and comparison of families of metabolic networks. Our main objective is therefore to develop new tools for the comparison of species strains at the metabolic level.

Consortium level: exploring the diversity of community consortia A new emerging field is system ecology, which aims at building predictive models of species interactions within an ecosystem for deciphering cooperative and competitive relationships between species 45. This field raises two new issues (1) uncertainty on the species present in the ecosystem and (2) uncertainty about the global objective governing an ecosystem. To address these challenges, our first research focus is the inference of metabolic exchanges and relationships for transporter identification, based on our expertise in metabolic network gap-filling. A second very challenging focus is the prediction of transporters families by obtaining refined characterization of transporters, which are quite unexplored apart from specific databases 53.

3.3.2 Associated software tools

Protomata5 is a machine learning suite for the inference of automata characterizing (functional) families of proteins at the sequence level. It provides programs to build a new kind of sequences alignments (said partial and local), learn automata and search for new family members in sequence databases. By enabling to model dependencies between positions, automata are more expressive than classical tools (PSSMs, Profile HMMs, or Prosite Patterns) and are well suited to predict new family members with a high specificity. This suite is for instance embedded in the cyanolase database  40 to automate its updade and was used for refining the classification of HAD enzymes 6 or identify shared conservations in the core proteome of extracellular vesicles produced by human and animal S. aureus strains 26.

AuReMe workspace is designed for tractable reconstruction of metabolic networks6 The toolbox allows for the Automatic Reconstruction of Metabolic networks based on the combination of multiple heterogeneous data and knowledge sources 1. The main added-values are the inclusion of graph-based tools relevant for the study of non-classical organisms (Meneco and Menetools packages), the possibility to trace the reconstruction and curation procedures (Padmet and Padmet-utils packages), and the exploration of reconstructed metabolic networks with wikis (wiki-export package, see: http://aureme.genouest.org/wiki.html). It also generated outputs to explore resulting networks with Askomics. It has been used for reconstructing metabolic networks of micro and macro-algae 51, extremophile bacteria 42 and communities of organisms4.

Mpwt is a Python package for running Pathway Tools7 on multiple genomes using multiprocessing. Pathway Tools is a comprehensive systems biology software system that is associated with the BioCyc database collection8. Pathway Tools is very used for reconstructing metabolic networks.

Metage2metabo is a Python tool to perform graph-based metabolic analysis starting from annotated genomes (reference genomes or metagenome-assembled genomes). It uses Mpwt to reconstruct metabolic networks for a large number of genomes. The obtained metabolic networks are then analyzed individually and collectively in order to get the added value of metabolic cooperation in microbiota over individual metabolism and to identify and screen interesting organisms among all.

3.4.1 Research topics

Genomic level: characterizing gene structure with grammatical languages and conservation information The subject here is to accurately represent gene structure, including intron/exon structure, for predicting the products of genes, such as isoform transcripts, and comparing the expression potential of a eukaryotic gene according to its context (e.g. tissue) or according to the species. Our approach consists in designing grammatical and comparative-genomics based models for gene structures able to detect heterogeneous functional sites (splicing sites, regulatory binding sites...), functional regions (exons, promotors...) and global constraints (translation into proteins) 36. Accurate gene models are defined by identifying general constraints shaping gene families and their structures conserved over evolution. Syntactic elements controlling gene expression (transcription factor binding sites controlling transcription; enhancers and silencers controlling splicing events...), i.e. short, degenerated and overlapping functional sequences, are modeled by relying on the high capability of SVG grammars to deal with structure and ambiguity 54.

System level: extracting causal signatures of complex phenotypes with systems biology frameworks The main challenge we address is to set up a generic formalism to model inter-layer interactions in large-scale biological networks. To that goal, we have developed several types of abstractions: multi-experiments framework to learn and control signaling networks 11, multi-layer reactions in interaction graphs 37, and multi-layer information in large-scale Petri nets 33. Our main issues are to scale these approaches to standardized large-scale repositories by relying on the interoperable Linked Open Data (LOD) resources and to enrich them with ad-hoc regulations extracted from sequence-based analysis. This will allow us to characterize changes in system attractors induced by mutations and how they may be included in pathology signatures.

3.4.2 Associated software tools

Logol software is designed for complex pattern modelling and matching9. It is a swiss-army-knife for pattern matching on DNA/RNA/Protein sequences, based on expressive patterns which consist in a complex combination of motifs (such as degenerated strings) and structures (such as imperfect stem-loop ou repeats) 2. Logol key features are the possibilities (i) to divide a pattern description into several sub-patterns, (ii) to model long range dependencies, and (iii) to enable the use of ambiguous models or to permit the inclusion of negative conditions in a pattern definition. Therefore, Logol encompasses most of the features of specialized tools (Vmatch, Patmatch, Cutadapt, HMM) and enables interplays between several classes of patterns (motifs and structures), including stem-loop identification in CRISPR.

Caspo software Cell ASP Optimizer (Caspo) constitutes a pipeline for automated reasoning on logical signaling networks (learning, classifying, designing experimental perturbations, identifying controllers, take time-series into account)10. The software handles inherent experimental noise by enumerating all different logical networks which are compatible with a set of experimental observations 11. The main advantage is that it enables a complete study of logical network without requiring any linear constraint programs.

Cadbiom package aims at building and analyzing the asynchronous dynamics of enriched logical networks11. It is based on Guarded transition semantic and allows synchronization events to be investigated in large-scale biological networks 33.

For instance, it was designed to allow controler of phenotypes in large-scale knowledge databases (PID) to be curated and analyzed 5.

7.1.1 AskOmics

• Name: Convert tabulated data into RDF and create SPARQL queries intuitively and "on the fly".
• Keywords: RDF, SPARQL, Querying, Graph, LOD - Linked open data
• Functional Description: AskOmics aims at bridging the gap between end user data and the Linked (Open) Data cloud. It allows heterogeneous bioinformatics data (formatted as tabular files) to be loaded in a RDF triplestore and then be transparently and interactively queried. AskOmics is made of three software blocks: (1) a web interface for data import, allowing the creation of a local triplestore from user's datasheets and standard data, (2) an interactive web interface allowing "à la carte" query-building, (3) a server performing interactions with local and distant triplestores (queries execution, management of users parameters).
• News of the Year: 2020: (1) release 4.4.1, (2) extensive documentation, (3) creation of tutorials, (4) complete redesign of the website, (4) support for disjunction, (5) registration to the Institut Français de Bioinformatique (IFB) catalogue (https://biosphere.france-bioinformatique.fr/catalogue/appliance/166/), (5) support for LDAP authentication
• URL: https://github.com/askomics/askomics
• Authors: Charles Bettembourg, Xavier Garnier, Anthony Bretaudeau, Fabrice Legeai, Olivier Dameron, Olivier Filangi, Yvanne Chaussin
• Contacts: Olivier Dameron, Anthony Bretaudeau, Fabrice Legeai
• Partners: Université de Rennes 1, CNRS, INRA

7.1.2 AuReMe

• Name: Automatic Reconstruction of Metabolic networks
• Keywords: Workflow, Bioinformatics, Metabolic networks, Omic data, Toolbox, Data management
• Functional Description:

  AuReMe enables the reconstruction of metabolic networks from different sources based on sequence annotation, orthology, gap-filling and manual curation. The metabolic network is exported as a local wiki allowing to trace back all the steps and sources of the reconstruction. It is highly relevant for the study of non-model organisms, or the comparison of metabolic networks for different strains or a single organism.

  Five modules are composing AuReMe: 1) The Model-management PADmet module allows manipulating and traceing all metabolic data via a local database. 2) The meneco python package allows the gaps of a metabolic network to be filled by using a topological approach that implements a logical programming approach to solve a combinatorial problem 3) The shogen python package allows genome and metabolic network to be aligned in order to identify genome units which contain a large density of genes coding for enzymes, it also implements a logical programming approach. 4) The manual curation assistance PADmet module allows the reported metabolic networks and their metadata to be curated. 5) The Wiki-export PADmet module enables the export of the metabolic network and its functional genomic unit as a local wiki platform allowing a user-friendly investigation.

• Release Contributions: - Reworking padmet and padmet-utils to allow full-python workflow in the future - Adding new script padmet-utils/exploration/prot2genome with exonerate - Fixing minor errors
• News of the Year: (1) Pantograph replaced by OrthoFinder (2) Create a readthedocs for AuReMe, padmet and padmet-utils (3) Reworking padmet and padmet-utils to allow full python workflow (4) Adding new script padmet-utils/exploration/prot2genome with exonerate (5) Modify template data structure (6) Fixing errors
• URL: http://aureme.genouest.org/
• Publication: hal-01807842
• Authors: Jeanne Cambefort, Anne Siegel, Alejandro Maass, Marie Chevallier, Meziane Aite, Guillaume Collet, Nicolas Loira, Sylvain Prigent
• Contacts: Jeanne Cambefort, Meziane Aite, Marie Chevallier
• Participants: Marie Chevallier, Meziane Aite, Guillaume Collet, Nicolas Loira, Sylvain Prigent, Jeanne Cambefort, Anne Siegel, Alejandro Maass
• Partner: University of Chile

7.1.3 Metage2Metabo

• Keywords: Metabolic networks, Microbiota, Metagenomics, Workflow
• Functional Description: Metabolic networks are graphs which nodes are compounds and edges are biochemical reactions. To study the metabolic capabilities of microbiota, Metage2Metabo uses multiprocessing to reconstruct metabolic networks at large-scale. The individual and collective metabolic capabilities (number of compounds producible) are computed and compared. From these comparisons, a set of compounds only producible by the community is created. These newly producible compounds are used to find minimal communities that can produce them. From these communities, the keytstone species in the production of these compounds are identified.
• News of the Year: First release.
• URL: https://github.com/AuReMe/metage2metabo
• Publication: hal-02395024
• Authors: Clémence Frioux, Arnaud Belcour, Anne Siegel
• Contacts: Arnaud Belcour, Clémence Frioux, Anne Siegel
• Participants: Clémence Frioux, Arnaud Belcour, Anne Siegel

7.1.4 Pathmodel

• Keywords: ASP - Answer Set Programming, Metabolic networks, Metabolic Pathway Drift, Bioinformatics, Systems Biology, Metabolomics
• Scientific Description: This tool is a prototype of the Metabolic Pathway Drift concept. This concept states that metabolic pathways undergo substantial turnover. The reactions involved in a pathway can change between species (change in reaction order or replacement of an enzyme by another one). Another goal of this tool is to linked genomics and metabolomics data. To implement this concept, Pathmodel uses the Answer Set Programming language. The input are the reactants and products involved in the pathway, known reactions occurring between these molecules, known m/z ratio, known domains shard by these molecules, an initial molecule and a goal molecule. Using these data, Pathmodel will infer reactions between molecules to reach the goal molecule using the known reactions. The result consist of potential alternative pathways for the studied organism.
• Functional Description: A metabolic pathway is a series of biochemical reactions. These reactions modify metabolites in order to synthesize a new metabolite or to produce energy. One difficulty when dealing with pathways in non-model organism is their incomplete conservation during evolution. To deal with this problem, we developed a prototype inferring new biochemical reactions using reactions and metabolites from known metabolic pathways and metabolomics data. This method produce alternative pathway that could occurred in the species of interest.
• Release Contributions: Fix an issue with test data.
• News of the Year: (1) Add a container in Singularity Hub (https://singularity-hub.org/collections/3758). (2) Rewrite data files (sterol and MAA). (3) Add creation of pictures of new molecules from MZ. (4) Add new output files to ease understanding of PathModel output. (5) Rewrite the Readme.
• URL: https://github.com/pathmodel
• Publication: hal-01943880
• Contacts: Arnaud Belcour, Gabriel Markov, Anne Siegel
• Participants: Arnaud Belcour, Jacques Nicolas, Gabriel Markov, Anne Siegel
• Partner: Station Biologique de Roscoff

7.1.5 CADBIOM

• Name: Computer Aided Design of Biological Models
• Keywords: Health, Biology, Biotechnology, Bioinformatics, Systems Biology
• Functional Description: The Cadbiom software provides a formal framework to help the modeling of biological systems such as cell signaling network with Guarder Transition Semantics. It allows synchronization events to be investigated in biological networks among large-scale network in order to extract signature of controllers of a phenotype. Three modules are composing Cadbiom. 1) The Cadbiom graphical interface is useful to build and study moderate size models. It provides exploration, simulation and checking. For large-scale models, Cadbiom also allows to focus on specific nodes of interest. 2) The Cadbiom API allows a model to be loaded, performing static analysis and checking temporal properties on a finite horizon in the future or in the past. 3) Exploring large-scale knowledge repositories, since the translations of the large-scale PID repository (about 10,000 curated interactions) have been translated into the Cadbiom formalism.
• News of the Year:

  - Comprehensive command line to run the calculations and analyze the generated results. - Module designed to produce models through the interpretation of various databases or ontologies, formalized according to the BioPAX standard. - Update of the site and the documentation.

  We recently developed a framework that integrates an updated version of the CADBIOM core software and visualization tools. We provided a command line interface allowing users to translate the interactions between biomolecules described in data sources in BioPAX format into the formalism based on the guarded transitions used by CADBIOM. The command line also makes it easy to search for scenarios based on the constraints of a model, to compare scenarios with each other and to visualize interaction graphs facilitating the biologist’s expertise (Vignet et al 2019 JOBIM)

• URL: http://cadbiom.genouest.org
• Authors: Michel Le Borgne, Geoffroy Andrieux, Nolwenn Le Meur, Nathalie Theret, Pierre Vignet
• Contact: Anne Siegel
• Participants: Geoffroy Andrieux, Michel Le Borgne, Nathalie Theret, Nolwenn Le Meur, Pierre Vignet, Anne Siegel

7.1.6 PPsuite

• Keywords: Proteins, Sequence alignment, Bioinformatics, Machine learning, Homology search
• Functional Description: This suite contains the following tools : - PPalign aligns Potts models and corresponding sequences - MakePotts builds a Potts model from a sequence or a multiple sequence alignment - VizPotts allows to visualize inferred Potts models - VizContacts allows to visualize inferred couplings with respect to actual contacts in a 3D protein structure.
• URL: https://www-dyliss.irisa.fr/ppalign/
• Publications: hal-03134517, hal-02862213, hal-02402646
• Authors: Hugo Talibart, François Coste
• Contacts: Hugo Talibart, François Coste

7.1.7 pax2graphml

• Name: pax2graphml - Large-scale Regulation Network in Python using BIOPAX and Graphml
• Keyword: Bioinformatics
• Functional Description: PAX2GRAPHML is an open source python library that allows to easily manipulate BioPAX source files as regulated reaction graphs described in .graphml format. PAX2GRAPHML is highly flexible and allows generating graphs of regulated reactions from a single BioPAX source or by combining and filtering BioPAX sources. Supported by the graph exchange format .graphml, the large-scale graphs produced from one or more data sources can be further analyzed with PAX2GRAPHML or standard python and R graph libraries.
• URL: https://pax2graphml.genouest.org/
• Contacts: François Moreews, Emmanuelle Becker, Anne Siegel
• Partner: INRAE

SANOFI: co-supervised PhD

Participants: Emmanuelle Becker, Olivier Dameron, Anne Siegel, Méline Wéry.

CIFRE co-supervised Grant: PhD. funding. 2017-2020

INSILIANCE: co-supervised PhD

Participants: Méziane Aite, Olivier Dameron.

10.1.1 Inria International Labs

Visits of international scientists

Oumarou Abdou Arbi (Université Dan Dicko Dankoulodo Maradi, Niger) was scheduled to visit Dyliss in February and March 2020. His visit was canceled due to covid-related restrictions and the collaboration continued remotely.

Collaborations with major European organizations

We initiated an informal collaboration with Lydie Lane and the Calipho group (creators of the neXtProt knowledge base15) at SIB Genève. We explored two topics. The first focused on determing whether neXtProt's public SPARQL endpoint can be used as a remote resource allowing AskOmics' users to extend their data with the neXtProt knowledge base and perform semantically-rich federated queries. The second stemmed from the observation that just like most life science resources exposed as SPARQL endpoints, neXtProt data structure is complex, which hampers their adoption by users. However, neXtProt benefits from a large base of example SPARQL queries designed for educational purposes. We explored whether this set of queries can be used to identify modules repeated in several queries, with the hypothesis that these modules provide an abstraction corresponding to a functional view on the neXtProt knowledge base. This work was supported by Maxime Léger's 3rd year BSc internship and his 1st year MSc internship.

IDEALG (ANR/PIA-Biotechnology and Bioresource)

Participants: Méziane Aite, Arnaud Belcour, François Coste, Clémence Frioux, Jeanne Got, Anne Siegel, Hugo Talibart.

The project gathers 18 partners from Station Biologique de Roscoff (coordinator), CNRS, IFREMER, UEB, UBO, UBS, ENSCR, University of Nantes, INRA, AgroCampus, and the industrial field in order to foster biotechnology applications within the seaweed field. Dyliss is co-leader of the WP related to the establishment of a virtual platform for integrating omics studies on seaweed and the integrative analysis of seaweed metabolism. Major objectives are the building of brown algae metabolic maps, metabolic flux analysis and the selection of symbiotic bacteria for brown algae. We will also contribute to the prediction of specific enzymes (sulfatases and haloacid dehalogenase)16. 2012–21. Total grant: 11M€. Dyliss grant: 534k€.

PhenomiR

Participants: Emmanuelle Becker, Olivier Dameron, Leo Mihlade, Anne Siegel.

The objective of the PhenomiR project is to propose an innovative solution for non-invasive phenotyping by analysing circulating microRNAs (miRNAs) (present in plasma) or present in biological fluids (coelomic fluid) and identify relevant biomarkers by the integration of omics data at multiple layers and to test to what extent the miRNAs of interest in trout are well conserved in fish genomes that are relatively complete. The PhenomiR project is carried out on rainbow trout (Oncorhynchus mykiss) which is both a major/principal production for the French fish farming industry and also a historical model species for INRA and the research laboratories involved in the fields of physiology, nutrition, well-being/behaviour and infectiology/immunology. 2019–22.

10.4.1 Programs funded by Inria

Prolific

Participants: Corentin Raphalen, Jeanne Got, Anne Siegel.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Seminar at the "Microbiome Day", Rennes, Using metabolic complementarity in microbiome to understand interactions among organisms [A. Belcour]
• Seminar at the Computer Science Department of ENS Paris. Des systèmes dynamiques à la biologie des systèmes pour des organismes non conventionnels: place du raisonnement symbolique. [A. Siegel]
• Seminar at the Swiss Institute of Bioinformatics Geneva, team Calipho AskOmics for integrating and querying life science data [X. Garnier and O. Dameron]
• Webinar "Using ontologies to improve animal science research" INRAe Bio-ontologies for complex life science data integration and reuse [O. Dameron]

11.1.5 Scientific expertise

11.1.6 Research administration

11.2.1 Teaching track responsibilities

• Coordination of the doctoral school "Biology and Health" of University of Bretagne Loire, Rennes Site 1 [N. Théret]
• Coordination of the master degree "Bioinformatics", Univ. Rennes 1 [E. Becker, O. Dameron]

11.2.2 Course responsibilities

• "Method", Master 2 in Computer Sciences, Univ. Rennes 1 [E. Becker]
• "Statistiques appliquées", 3rd year in Fundamental Computer Sciences, ENS Rennes [E. Becker]
• "Introduction to computational ecology", Master 2 in Ecology, Univ. Rennes 1 [E. Becker]
• "Object oriented programming", Master 1 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Advanced R for data analysis", Master 1 in Ecology + Master 1 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Insertion Professionnelle et tables rondes", Master 1 and Master 2 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Atelier de Biostatistiques", 2nd year Biology, Univ Rennes 1 [E. Becker]
• "Internship", Master 1 in Computer Sciences, Univ. Rennes 1 [C. Belleannée]
• "Supervised machine learning", Master 2 in Computer Sciences, Univ Rennes 1 [F. Coste]
• "Complément informatique 1", Licence 1 informatique, Univ. Rennes 1 [O. Dameron]
• "Atelier bioinformatique", Licence 2 informatique, Univ. Rennes 1 [O. Dameron]
• "Intégration: Remise à niveau en informatique", Master 1 in bioinformatics, Univ. Rennes 1 [O. Dameron]
• "Semantic Web and bio-ontologies", Master 2 in bioinformatics, Univ. Rennes 1 [O. Dameron]
• "Internship", Master 2 in bioinformatics, Univ. Rennes 1 [O. Dameron]
• "Integrative and Systems biology", Master 2 in bioinformatics, Univ. Rennes 1 [A. Siegel]
• "Micro-environnement Cellulaire normal & pathologique", Master 2 Biologie cellulaire et Moléculaire, Univ. Rennes 1 [N. Théret]

11.2.3 Teaching

• Licence : E. Becker, "Atelier de Biostatistiques", 34h, 2nd year in Biology, Univ. Rennes 1, France
• Licence : E. Becker, "Statistiques Appliquées", 20h, 3rd year in Fundamental Computer Sciences, ENS Rennes, France
• Master : E. Becker, "Object oriented programming", 56h, Master 1 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Advanced R for data analysis", 36h, Master 1 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Introduction to computational ecology", 34h, Master 2 in Ecology, Univ. Rennes 1, France
• Master : E. Becker, "Method", 15h, Master 2 in Computer Sciences, Univ. Rennes 1, France
• Master : E. Becker, "Insertion Professionnelle et tables rondes", 6h, Master 1 and Master 2 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Systems Biology : biological netorks", 27h, Master 2 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Introduction to Bioinformatics", 3h, Master MEEF Biology, Univ. Rennes 1, France.
• Licence: C. Belleannée, Langages formels, 20h, L3 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Projet professionnel et communication, 16h, L1 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Enseignant référent, 20h, L1 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Spécialité informatique : Functional and immutable programming , 42h, L1 informatique, Univ. Rennes 1, France
• Master: C. Belleannée, Algorithmique du texte et bioinformatique, 10h, M1 informatique, Univ. Rennes 1, France
• Master: C. Belleannée, Programmation logique et contraintes, 32h, M1 informatique, Univ. Rennes 1, France
• Master: S. Blanquart, Juries of Master 2 in bioinformatics, 10h, Univ. Rennes 1, France
• Master: L. Bourneuf, Projet, 25h, M1 Santé Publique, France.
• Master: F. Coste, Supervised machine learning, 10h, M2 Science Informatique, Univ. Rennes, France
• Licence: O. Dameron, Biostatistiques, 12h, 1st year school of medicine, Univ. Rennes 1, France
• Licence: O. Dameron, "Programmation 1", 40h, Licence 1 informatique, Univ. Rennes 1, France
• Licence: O. Dameron, "Complément informatique", 24h, Licence 1 informatique, Univ. Rennes 1, France
• Licence: O. Dameron, "Atelier bioinformatique", 12h, Licence 2 informatique, Univ. Rennes 1, France
• Licence: O. Dameron, "Programmation", 54h, Licence 3 miage, Univ. Rennes 1, France
• Master: O. Dameron, "Intégration: Remise à niveau en informatique", 18h Master 1 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, "Programmation impérative en Python", 98h Master 1 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, "Système informatique GNU/Linux", 10h, Master 1 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, 5h, "Internship", Master 1 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, "Bases de mathématiques et probablité", 9h, Master1 in public health, Univ. Rennes 1, France
• Master: O. Dameron, "Programmation impérative en Python (2)", 3h Master 1 in public health, Univ. Rennes 1, France
• Master: O. Dameron, 20h, "Semantic Web and bio-ontologies", Master 2 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, 15h, "Internship", Master 2 in bioinformatics, Univ. Rennes 1, France
• Licence: N. Guillaudeux, Projet professionnel et communication, 16h, 1st year Computer Science, Univ. Rennes 1, France
• Licence: N. Guillaudeux, "TPs Python", 36h, 1st year in Biology, Univ. Rennes 1, France
• Licence: M. Louarn, Introduction à la BioInformatique, 6h, L2 Informatique, Univ. Rennes 1, France.
• Licence: M. Louarn, Informatique, 10h, L1 Physique Chimie, Univ. Rennes 1, France.
• Master: M. Louarn, Informatique Médicale Avancée, 2h, M1 Médecine, Univ. Rennes 1, France.
• Master: M. Louarn, Object-oriented programming, 25h, M2 bioinformatique et génomique, Univ. Rennes 1, France.
• Master: M. Louarn, Jury de stage, 6h, M1 bioinformatique et génomique, Univ. Rennes 1, France.
• Master: A. Siegel, Integrative and Systems biology, Master 2 in bioinformatics, Univ. Rennes 1.

11.2.4 Supervision

• PhD: Marine Louarn, Intégration de données génomiques massives et hétérogènes, application aux mutations non-codantes dans le lymphome folliculaire, defended November 26th 2020, supervised by A. Siegel, T. Fest (CHU) and O. Dameron. 29
• PhD: Méline Wéry, Methodology development in disease treatment projects. , defended December 16th 2020, supervised by E. Becker, C. Bettembourg (Sanofi), O. Dameron, and A. Siegel. 30
• PhD in progress : Hugo Talibart, Learning grammars with long-distance correlations on proteins, started in Nov. 2017, supervised by F. Coste and J. Nicolas.
• PhD in progress: Mael Conan, Predictive approach to assess the genotoxicity of environmental contaminants during liver fibrosis, started in Oct. 2017, supervised by S. Langouet and A. Siegel.
• PhD in progress : Pierre Vignet, Identification et conception expérimentale de nouveaux agents thérapeutiques à partir d'un modèle informatique des réseaux d'influence du TGF-beta dans les pathologies hépatiques chroniques , started in Dec. 2018, supervised by N. Théret and A. Siegel.
• PhD in progress: Johanne Bakalara, Temporal models of care sequences for the exploration of medico-administrative data, started in Oct. 2018, supervised by T. Guyet (Lacodam), E. Oger (Repères) and O. Dameron.
• PhD in progress: Nicolas Guillaudeux, Compare gene structures to predict isoform transcripts, started in Oct. 2018, supervised by O. Dameron, S. Blanquart and C. Belleannée
• PhD in progress: Arnaud Belcour, Inferring Model metabolisms for bacterial ecosystems reduction, started in Oct. 2019, supervised by A. Siegel and S. Blanquart.
• PhD in progress: Olivier Dennler, Modular functional characterization of ADAMTL and ADAMTSL protein families, started in Oct. 2019, supervised by N. Theret, F. Coste and S. Blanquart.
• PhD in progress: Virgilio Kmetzsch Multi-modal analysis of neuroimaging and transcriptomics data in genetically-induced fronto-temporal dementia, started in Oct. 2019, supervised by E. Becker and O. Colliot (INRIA Aramis, ICM Paris)
• PhD in progress : Mathieu Bouguéon. Modélisation prédictive pour le ciblage thérapeutique du TGF-beta dans les pathologies chroniques hépatiques, started in Oct. 2020, supervised by N. Théret and A. Siegel
• PhD in progress : Nicolas Buton. Deep learning for proteins functional annotation : novel architectures and interpretability methods, started in Oct. 2020, supervised by F. Coste, Y. Le Cunff and O. Dameron
• PhD in progress : Camille Juigné. Analyse des données biologiques hétérogènes par exploitation de graphes multicouches pour comprendre et prédire les variations d’efficacité alimentaire chez le porc , started in Dec. 2020, supervised by E. Becker and F. Gondret (INRAE Pegase).
• PhD in progress : Méziane Aite. Identification de nouvelles combinaisons thérapeutiques dans les indications neurologiques, started in Nov. 2020, supervised by O. Dameron and V. Lafon (Insiliance).

11.2.5 Juries

• Referee of PhD thesis (1): K. Guillaumier, University of Malta [F. Coste]
• Member of PhD thesis juries (6): M. Wery, Université de Rennes 1 [E. Becker, O. Dameron, A. Siegel], M. Louarn, Université de Rennes 1 [O. Dameron, A. Siegel], A. Aubert, Université Lyon 1 [N. Théret], R. Gazzotti, Université Côte d'Azur [O. Dameron], C. Dalloux, Université de Rennes 1 [O. Dameron], W. Delage, Université de Rennes 1 [O. Dameron, president]
• Referee of habilitation thesis (0):
• Member of habilitation thesis juries (1): V. Claveau, Université de Rennes 1 [O. Dameron]

11.2.6 Interns

• Internship, from May until Jun 2020. Supervised by F. Coste. Student: Malo Revel (Licence 3, ENS Rennes). Subject: Learning k,l-locally substitutable Multiple Context-Free Grammars
• Internship, from Feb until Aug 2020. Supervised by F. Coste and Y. Le Cunff. Student: Nicolas Buton (Master 2, Sorbonne University). Subject: Deep Learning Encoder Representations from Transformers: from Natural Languages to Protein Sequences Processing 31
• Internship, from Jan until July 2020. Supervized by F. Moreews and E. Becker. Student : Quentin Delhon (Université de Rennes 1). Subject: Graph exploration algorithms to search for key regulators of metabolic pathways
• Internship, from Jan until July 2020. Supervized by E. Becker, O. Dameron and G. Rabut (IGDR). Student : Marc Melkonian (Université de Rennes 1). Subject: Réseaux biologiques : intégration de données d'interactions homogènes et hétérogènes
• Internship, from Jan until July 2020. Supervized by A. Siegel and H. Falentin. Student : Corentin Raphalen (Université de Rennes 1). Subject: Sélection de consortia microbiens optimaux pour la synthèse de vitamines dans les produits laitiers et fermentés
• Internship, from Jan until July 2020. Supervized by A. Siegel. Student : Pierre Gueracher (Université de Rennes 1). Subject: Variabilité des regroupements issus de l'analyse de données à cellule unique : impact du choix des paramètres et des marqueurs de types cellulaires
• Internship, from March until July 2020. Supervized by A. Belcour, S. Blanquart and A. Siegel. Student : Baptiste Ruiz (Agrocampus Paris). Subject: Modélisation de la diversité microbienne mesurée dans des expérimentations de fermenteurs
• Internship, from January until March 2020. Supervized by A. Siegel. Student : Adriana Concha (University of Chile). Subject: Integration of metagenomic datasets of Atacama desert at the metabolic scale.
• Internship, from April until June 2020. Supervized by O. Dameron. Student : Christophe Héligon (Université de Rennes 1). Subject: Integrating gene-phenotype association data using Semantic Web technologies.
• Internship, from May until July 2020. Supervized by O. Dameron. Student : Maxime Léger (Université de Rennes 1). Subject: Identification de modules au sein d'une collection de requêtes SPARQL.

11.3.1 Education

• Operation DECLIC (Dialogues Entre Chercheurs et Lycéens pour les Intéresser à la Construction des Savoirs). Lycée Descartes, Rennes. [N. Théret, M. Conan, O. Dennler]
• "Elles codent Elles créent" Several PhD students have been involved in creative Python learning sessions for female students in two high-schools. [M. Louarn, M. Wéry, A. Siegel]
• Formation des étudiants au capes informatique Sensibilisation aux stéreotypes en informatique [A. Siegel]

11.3.2 Interventions

• Rendez-Vous des Jeunes Mathématiciennes et Informaticiennes de l’ENS Rennes, 18 novembre. Atelier "déconstruction des stéreotypes" [A. Siegel]
• Rendez-Vous des Jeunes Mathématiciennes et Informaticiennes de l’ENS, 12 décembre. Informatique et biologie moléculaire: la place des modèles mathématiques [A. Siegel]

International journals

• 12 articleF. Azar, K. Courtet, B. Dekky, D. Bonnier, O. Dameron, A. Colige, V. Legagneux and N. Théret. Integration of miRNA‐regulatory networks in hepatic stellate cells identifies TIMP3 as a key factor in chronic liver diseaseLiver International408August 2020, 2021-2033
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• 13 article A. Belcour, C. Frioux, M. Aite, A. Bretaudeau, F. Hildebrand and A. Siegel. Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species eLife 9 December 2020
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• 14 articleA. Belcour, J. Girard, M. Aite, L. Delage, C. Trottier, C. Marteau, C.-J. Leroux, S. Dittami, P. Sauleau, E. Corre, J. Nicolas, C. Boyen, C. Leblanc, J. Collén, A. Siegel and G. Markov. Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway DriftiScience232February 2020, 100849
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• 15 articleB. Burgunter-Delamare, H. Kleinjan, C. Frioux, E. Fremy, M. Wagner, E. Corre, A. Le Salver, C. Leroux, C. Leblanc, C. Boyen, A. Siegel and S. Dittami. Metabolic Complementarity Between a Brown Alga and Associated Cultivable Bacteria Provide Indications of Beneficial InteractionsFrontiers in Marine Science7February 2020, 1-11
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• 16 articleM. Conan, S. Langouët, A. Siegel and N. Théret. Innovative Approach to Predict DNA Adduct Formation of Environnemental ContaminantsEnvironmental and Molecular Mutagenesis61S12020, 58
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• 17 articleS. Daval, K. Gazengel, A. Belcour, J. Linglin, A.-Y. Guillerm-Erckelboudt, A. Sarniguet, M. Manzanares-Dauleux, L. Lebreton and C. Mougel. Soil microbiota influences clubroot disease by modulating Plasmodiophora brassicae and Brassica napus transcriptomesMicrobial Biotechnology1352020, 1648-1672
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• 18 articleS. Dittami, E. Corre, L. Brillet-Guéguen, A. Lipinska, N. Pontoizeau, M. Aite, K. Avia, C. Caron, C. Cho, J. Collen, A. Cormier, L. Delage, S. Doubleau, C. Frioux, A. Gobet, I. González-Navarrete, A. Groisillier, C. Herve, D. Jollivet, H. Kleinjan, C. Leblanc, X. Liu, D. Marie, G. Markov, A. Minoche, M. Monsoor, P. Péricard, M.-M. Perrineau, A. Peters, A. Siegel, A. Siméon, C. Trottier, H. Yoon, H. Himmelbauer, C. Boyen and T. Tonon. The genome of Ectocarpus subulatus – A highly stress-tolerant brown algaMarine GenomicsJanuary 2020, 1-24
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• 19 articleM. Folschette, V. Legagneux, A. Poret, L. Chebouba, C. Guziolowski and N. Théret. A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinomaBMC Bioinformatics211January 2020, 18
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• 20 articleC. Frioux, S. Dittami and A. Siegel. Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactionsBiochemical Society TransactionsMay 2020, 1-19
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• 21 article L. Garczarek, U. Guyet, H. Doré, G. Farrant, M. Hoebeke, L. Brillet-Guéguen, A. Bisch, M. Ferrieux, J. Siltanen, E. Corre, G. Le Corguillé, M. Ratin, F. Pitt, M. Ostrowski, M. Conan, A. Siegel, K. Labadie, J.-M. Aury, P. Wincker, D. Scanlan and F. Partensky. Cyanorak v2.1: a scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes Nucleic Acids Research October 2020
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• 22 article U. Guyet, N. Nguyen, H. Doré, J. Haguait, J. Pittera, M. Conan, M. Ratin, E. Corre, G. Le Corguillé, L. Brillet-Guéguen, M. Hoebeke, C. Six, C. Steglich, A. Siegel, D. Eveillard, F. Partensky and L. Garczarek. Synergic Effects of Temperature and Irradiance on the Physiology of the Marine Synechococcus Strain WH7803 Frontiers in Microbiology 11 July 2020
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• 23 article V. Henry, I. Moszer, O. Dameron, L. Vila Xicota, B. Dubois, M.-C. Potier, M. Hofmann-Apitius and O. Colliot. Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer’s disease Database - The journal of Biological Databases and Curation February 2021
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• 24 articleV. Kmetzsch, V. Anquetil, D. Saracino, D. Rinaldi, A. Camuzat, T. Gareau, L. Jornea, S. Forlani, P. Couratier, D. Wallon, F. Pasquier, N. Robil, P. De La Grange, I. Moszer, I. Le Ber, O. Colliot and E. Becker. Plasma microRNA signature in presymptomatic and symptomatic subjects with C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosisJournal of Neurology, Neurosurgery and PsychiatryNovember 2020, jnnp-2020-324647
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• 25 articleD. Mandakovic, Á. Cintolesi, J. Maldonado, S. Mendoza, M. Aite, A. Gaete, F. Saitua, M. Allende, V. Cambiazo, A. Siegel, A. Maass, M. Gonzalez and M. Latorre. Genome-scale metabolic models of Microbacterium species isolated from a high altitude desert environmentScientific Reports101December 2020, 1-12
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• 26 articleN. Tartaglia, A. Nicolas, V. DE REZENDE RODOVALHO, B. Luz, V. Briard-Bion, Z. Krupova, A. Thierry, F. Coste, A. Burel, P. Martin, J. Jardin, V. Azevedo, Y. Le Loir and E. Guedon. Extracellular vesicles produced by human and animal Staphylococcus aureus strains share a highly conserved core proteomeScientific Reports101April 2020, 1-13
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International peer-reviewed conferences

• 27 inproceedingsH. Talibart and F. Coste. ComPotts: Optimal alignment of coevolutionary models for protein sequencesJOBIM 2020 - Journées Ouvertes Biologie, Informatique et MathématiquesMontpellier, FranceJune 2020, 1-8
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Conferences without proceedings

Scientific book chapters

• 28 inbookN. Théret, J. Feret, A. Hodgkinson, P. Boutillier, P. Vignet and O. Radulescu. Integrative models for TGF-$$ signaling and extracellular matrix'7Extracellular Matrix OmicsBiology of Extracellular Matrixhttps://v6ediss.universite-lyon.fr/sylvie-ricard-blum--32497.kjspDecember 2020, 17
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Doctoral dissertations and habilitation theses

• 29 thesis M. Louarn. Analysis and integration of heterogeneous large-scale genomics data Université Rennes 1 November 2020
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• 30 thesis M. Wery. Identification of causal pathologic signature by multi-omic data integration Université de Rennes 1 December 2020
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Reports & preprints

Other scientific publications

• 31 thesis N. Buton. Rapport de stage Master 2 : Apprentissage et étude de Transformer pour la classification en familles protéiques Sorbonne université September 2020
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• 32 misc C. Frioux, A. Belcour, M. Aite, A. Bretaudeau, F. Hildebrand and A. Siegel. Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone species Montpellier, France June 2020
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