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  72, 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 a functional static map of biological models that can be analyzed and/or compressed. This requires to link 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 (1) to take advantage of the information available in the LOD cloud for analyzing their own data, and (2) to 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.

Pax2graphml aims at easily manipulating BioPAX source files as regulated reaction graphs described in graph format. The goal is to be highly flexible and to integrate graphs of regulated reactions from a single BioPAX source or by combining and filtering BioPAX sources. The output graphs can then be analyzed with additional tools developed in the team, such as KeyRegulatorFinder.

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 the functionality allowing 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 that take a sample of functional sequences as input and infer a model representing their key syntactical characteristics, including dependencies between residues.

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

Non-model organisms often lack both complete and reliable annotated sequences, which cause the draft networks of their metabolism to largely suffer from incompleteness. In former studies, the team has developed several methods to improve the quality of eukaryotic 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 The newly emerging field of system ecology aims at building predictive models of species interactions within an ecosystem, with the goal of deciphering cooperative and competitive relationships between species 51. 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. The second challenging focus is the prediction of transporters families via refined characterization of transporters, which are quite unexplored apart from specific databases 64.

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 sequence alignments (characterized as partial and local), learn automata, and search for new family members in sequence databases. By enabling to model local 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 41 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 69.

PPSuite6 is one of the first frameworks taking into account coevolutionary dependencies between residues for the comparison of protein sequences. It proposes a complete workflow enabling to infer direct couplings between the positions of a sequence of interest by a Potts model with the help of the sequence close homologs and to score the similarity of the sequences by alignment of the inferred Potts models, as well as tools to visualize the models and their alignments 68, 67.

AuReMe and AuCoMe workspaces is designed for tractable reconstruction of metabolic networks7. 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-model organisms (Meneco and Menetools packages), the possibility to trace the reconstruction and curation procedures (Padmet package), and the exploration of reconstructed metabolic networks with wikis (wiki-export package, see: aureme.genouest.org/wiki.html) 33. It also generates outputs to explore the resulting networks with Askomics. It has been used for reconstructing metabolic networks of micro and macro-algae 62, extremophile bacteria 44 and communities of organisms 4.

Mpwt, emmapper2gbk is a Python package for running Pathway Tools8 on multiple genomes using multiprocessing. Pathway Tools is a comprehensive systems biology software system that is associated with the BioCyc database collection9. Pathway Tools is frequently used for reconstructing metabolic networks. In order to allow the output of the eggnoggmapper annotation tool to be used by Mpwt, we also developed emmaper2gbk to create relevant genome files.

Metage2metabo is a Python tool to perform graph-based metabolic analysis starting from annotated genomes (reference genomes or metagenome-assembled genomes) 31. 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 goal 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 65.

System level: extracting causal signatures of complex phenotypes with systems biology frameworks Our main challenge 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 30. 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 modeling and matching10. 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 Cell ASP Optimizer (Caspo) software constitutes a pipeline for automated reasoning on logical signaling networks (learning, classifying, designing experimental perturbations, identifying controllers, take time-series into account)11. 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 networks12. It is based on Guarded transition semantic and allows synchronization events to be investigated in large-scale biological networks 30. For example, it allowed to analyze controler of phenotypes in a large-scale knowledge database (PID) 5.

Recently, we have significantly refactored Cadbiom package towards a framework that allows the identification of causal regulators in large-scale models, formalized in the BioPAX language and automatically interpreted as guarded transitions. The Cadbiom framework was applied to the BioPAX version of two ressources (PID, KEGG) of the PathwayCommons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize the causal signatures of markers of the epithelial-mesenchymal transition.

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).
• URL:
  https://­askomics.­org/
• Contact:
  Olivier Dameron
• Partners:
  Université de Rennes 1, CNRS, INRA

7.1.2 Regulus

• Keywords:
  Bioinformatics, Semantic Web
• Functional Description:
  Regulus, software which computes TF-gene regulation relations (TF for transcriptional regulatory) and qualify them as activation or inhibition. Its main principles are to (1) take into account regulatory factors (TFs and regions) activities, (2) discretize the activities into patterns, (3) produce signed circuits inferred by testing biological constraints likelihood and (4) be easly reusable and applicable to many datasets.
• News of the Year:
  Regulus is published.
• URL:
  https://­gitlab.­com/­teamDyliss/­regulus
• Publication:
  hal-04443527
• Contact:
  Olivier Dameron
• Participants:
  Marine Louarn, Guillaume Collet, Eve Barre, Thierry Fest, Olivier Dameron, Anne Siegel, Fabrice Chatonnet
• Partners:
  Université de Rennes 1, INSERM, University of British Columbia, Université de Lyon

7.1.3 Merrin

• Name:
  MEtabolic Regulation Rule INference from time series data
• Keywords:
  Bioinformatics, Rule-based programming, ASP - Answer Set Programming
• Functional Description:
  Merrin is Python3 software to infer regulatory rules of a regulated metabolic network (RMN) from possibly incomplete and noisy observed time series. Merrin is an extension of Caspo (https://github.com/bioasp/caspo) focused on the inference of regulations from indirect measures on metabolism. Merrin takes as input a metabolic network in SBML format, a set of regulatory proteins P, a set of observed time series in CSV format, and a Prior Knowledge Network (PKN) in a text file format. Merrin generates a CSV file describing the inferred regulatory networks.
• URL:
  https://­github.­com/­bioasp/­merrin
• Publication:
  hal-03701755
• Contact:
  Anne Siegel
• Participants:
  Kerian Thuillier, Caroline Baroukh, Alexander Bockmayr, Ludovic Cottret, Loic Paulevé, Anne Siegel
• Partners:
  Université de Rennes 1, Université de Bordeaux, Université de Toulouse, Freie Universität Berlin

7.1.4 Metage2Metabo

• Keywords:
  Metabolic networks, Microbiota, Metagenomics, Workflow
• Scientific Description:
  Flexible pipeline for the metabolic screening of large scale microbial communities described by reference genomes or metagenome-assembled genomes. The pipeline comprises several main steps. (1) Automatic and parallel reconstruction of metabolic networks. (2) Computation of individual metabolic potentials (3) Computation of collective metabolic potential (4) Calculation of the cooperation potential described as the set of metabolites producible by species only in a cooperative context (5) Computation of minimal-sized communities sastifying a metabolic objective (6) Extraction of key species (essential and alternative symbionts) associated to a metabolic function
• 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.
• URL:
  https://­github.­com/­AuReMe/­metage2metabo
• Publication:
  hal-02395024
• Contact:
  Clemence Frioux
• Participants:
  Clemence Frioux, Arnaud Belcour, Anne Siegel

7.1.5 AuCoMe

• Name:
  Automatic Comparison of Metabolisms
• Keywords:
  Bioinformatics, Workflow, Metabolic networks, Omic data, Data analysis
• Functional Description:
  AuCoMe is a Python package that aims at reconstructing homogeneous metabolic networks and pan-metabolism starting from genomes with heterogeneous levels of annotations. Four steps are composing AuCoMe. 1) It automatically infers annotated genomes from draft metabolic networks thanks to Pathway Tools and MPWT. 2) The Gene-Protein-Reaction (GPR) associations previously obtained are propagated to protein orthogroups in using Orthofinder and, an additional robustness criteria. 3) AuCoMe checking the presence of supplementary GPR associations by finding missing annotation in all genomes. In this step, the tools BlastP, TblastN and, Exonerate are called. 4) It adding spontaneous reactions to metabolic pathways that were completed by the previous steps. AuCoMe generates several outputs to facilitate the analysis of results: tabuled files, SBML files, PADMET files, supervenn and a dendogram of reactions.
• URL:
  https://­github.­com/­AuReMe/­aucome
• Publication:
  hal-03778267
• Contact:
  Anne Siegel
• Participants:
  Arnaud Belcour, Jeanne Got, Meziane Aite, Ludovic Delage, Jonas Collen, Clemence Frioux, Catherine Leblanc, Simon M. Dittami, Samuel Blanquart, Gabriel V. Markov, Anne Siegel

7.1.6 EsMeCaTa

• Name:
  EsMeCaTa
• Keywords:
  Metabolism, Taxonomies, Evolution, Metagenomics
• Functional Description:
  Sequencing methods allowed to detect organisms in environment samples. It is possible to identify the groups in which the organisms belong. But to infer metabolic networks and estimate metabolic interactions between organisms, new methods are needed. That's why we developed EsMeCaTa. This method takes as input taxonomic affiliations showing which organisms are present in the environment and then query UniProt to extract proteomes associated with these organisms. Then, the proteins are clustered into groups to detect if a cluster is present in multiple proteomes. It is possible to extract clusters which contain proteins present in multiple proteomes. Their functional annotations are then retrieved on UniProt, which allows to estimate the metabolic capacities of the taxon.
• URL:
  https://­github.­com/­AuReMe/­esmecata
• Publication:
  hal-03697249
• Contact:
  Samuel Blanquart

7.1.7 SPARTA

• Name:
  Shifting Paradigms to Annotation Representation from Taxonomy to identify Archetypes
• Keywords:
  Bioinformatics, Microbiota, Classification
• Functional Description:
  SPARTA can be launched following two steps. The first can be called with the "sparta esmecata'" command. This command takes as input a taxonomic abundance table and launches a run of the EsMeCaTa pipeline. The second part of the pipeline can be called with the "sparta classification" command. It takes as input a file containing the labels associated with each sample in the dataset, a functional and taxonomic description of the samples, a description of each taxon’s affiliation, and a table indicating the occurrence of functions in each organism. This last step involves the training of 20 successive Random Forest classifiers.
• URL:
  https://­github.­com/­baptisteruiz/­SPARTA
• Publication:
  hal-04816962
• Contact:
  Yann Le Cunff

7.1.8 Transformer Framework for Protein Characterization - EnzBert

• Keywords:
  Deep learning, Transformer, Functional annotation, Proteins, Biological sequences
• Scientific Description:
  A generic framework for the specialization of a pre-trained transformer protein language model for classification or regression tasks.
• Functional Description:
  Given examples of annotated sequences, this tool allows to train and analyse resulting models with respect to evaluation metrics (accuracy, correlation) plots and the importance of the residues for the inference. The process is fully automated and the whole operation can be done by modifying a JSON configuration file and providing a JSON data set. No code skills are thus required. This framework enabled training the EnzBert model which predicts the Enzyme Commission number of protein sequences.
• News of the Year:
  A new Enzbert model was trained and tested with the addition of two enzyme families of interest to the ABIE team at the Station Biologique de Roscoff.
• URL:
  https://­gitlab.­inria.­fr/­nbuton/­tfpc
• Publications:
  tel-04347632, hal-04382475
• Contact:
  Nicolas Buton
• Participants:
  Nicolas Buton, François Coste, Yann Le Cunff, Noe Robert

7.1.9 EnzBert-GO

• Keywords:
  Proteins, Biological sequences, Functional annotation, Deep learning, Ontologies
• Scientific Description:
  Code for learning and using BERT deep neural architectures for the prediction of multi-level and multi-class functional enzymatic GO annotations of protein sequences.
• Functional Description:
  Prediction of the functional enzymatic GO annotations of protein sequences
• News of the Year:
  EnzBert-GO is a fork of code originally developed by Nicolas Buton. It implements multi-class and multi-level enzymatic function prediction to address Enzbert's main practical limitations and utilize Gene Ontology's hierarchical annotations.
• URL:
  https://­gitlab.­inria.­fr/­fcoste/­enzbert-go
• Contact:
  François Coste
• Participant:
  François Coste

7.1.10 FUSE-PhyloTree

• Name:
  FUnctions and SEquence conservations on a Phylogenetic Tree
• Keywords:
  Bioinformatics, Biological sequences, Sequence alignment, Phylogenomics, Proteins
• Scientific Description:
  FUSE-PhyloTree is dedicated to estimate the sequence regions which are potentially associated to functions of interest in a multi-functional protein families, such as paralogous and multi-domain protein families. The method uses state-of -the-art programs to estimate a mapping of both the ancestral functions and the ancestral sequence content at each node in the phylogenetic family tree. It enables the association of functions with local sequence conservations through the inference of their co-appearance along the evolutionary gene tree, and it generates interactive Itol representations allowing to explore the annotated tree.
• Functional Description:
  FUSE-PhyloTree is dedicated to studying multi-functional protein families, such as paralogous and multi-domain protein families. It enables the association of functions with local sequence conservations through the inference and exploration of their ancestral co-appearance within the evolutionary tree of genes.
• News of the Year:
  To make the package public, we simplified the outputs, improved the documentation, and added helper scripts to facilitate its use by non-specialists. Additionally, we now provide a Singularity container alongside the Docker container to enable wider distribution.
• URL:
  https://­github.­com/­OcMalde/­fuse-phylotree
• Publication:
  hal-04248728
• Contact:
  François Coste
• Participants:
  Olivier Dennler, Elisa Chenel, François Coste, Samuel Blanquart, Catherine Belleannée, Nathalie Theret

BeCycle

Participants: Jeanne Got, Noé Robert, Anne Siegel.

In the context of the Grand Défi "Ferment du futur", this private-public project aims at scanning thousands of bacterial genomes to identify the best consortium of strains capable of producing metabolites of interest. Duration: 2024-2026, total of the grant 400k€.

10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

10.3.1 Other european programs/initiatives

PEPR Digital agro-ecology : HOLOBIONT

Participants: Juliette Francis, Yann Le Cunff.

Animals and their microbiota form a composite organism, called a holobiont, which can be considered the ultimate unit on which evolution and selection act. Host genes and the environment influence the colonization, development, and function of the various microbiota, which in turn help shape the host's phenotypes. The phenotypes of the holobiont thus result from the combined action of the host genes and those of its microbiota, and their determinism can be explored by implementing hologenetic approaches capable of considering host genomes and metagenomes jointly. The overall objective of this PEPR is to develop integrative hologenetic approaches for animal breeding, using state-of-the-art technologies to generate, process and analyze genetic and genomic datasets of the host and its microbiota as well as the phenotypes and environmental parameters in which the holobionts evolve. To this end, the project aims to develop methods for the analysis of new-generation phenotyping data of the holobiont (mainly high-throughput and continuous), for their modeling and for the analysis of their interrelationships with the microbiota data. Juliette Francis 's Ph.D, co-supervised by Yann Le Cunff (Dyliss) and Mahendra Mariadassou (INRAe, MaIAGe), focuses on co-analyzing genomic data, microbiota data and metabolomic data to efficiently predict a phenotype of interest (food intake efficiency in this case). 2024–2028. Dyliss grant : 178k€.

PEPR digital health : M4DI

Participants: Emmanuelle Becker, Océane Carpentier, Yann Le Cunff.

The main objective of the Methods and Models for Multimodal and Multiscale Data Integration (M4DI) project is to develop innovative methodological frameworks for the integration of biomedical datasets. In particular, the team is involved in designing robust machine learning approaches enhanced by prior knowledge. In particular, Océane Carpentier 's Ph.D, co-supervised by Emmanuelle Becker , Yann Le Cunff (Dyliss), Nicolas Jay and Aurélie Bannay (LORIA, Nancy) is dedicated to exploiting the ontology structure of the Gene Ontology database in machine learning algorithms. One key application will be carried out on a local cohort of Crohn's patients with the CHU of Rennes. 2024–2028. DYLISS grant: 169k€.

PEPR Digital health : ShareFAIR

Participants: Olivier Dameron, Ulysse Le Clanche, Yann Le Cunff.

The increasing availability of life science data offers unprecedented opportunities for healthcare research, it has the potential to revolutionize the way we understand and treat diseases, as it allows researchers to identify trends and patterns that may not have been apparent with smaller data sets. However, exploiting this potential requires innovative solutions for the annotation of biomedical and clinical datasets and extraction of provenance. Challenges thus include standardization and annotation for datasets and protocols, extracting protocols from text and datasets, and synthesizing them into interoperable, yet shareable protocols. ShareFAIR will provide (i) standards to uniformly annotate datasets and protocols with ontologies/common vocabularies and provenance to trace their origin, (ii) an interoperable framework to index, design and annotate reliable and shareable analysis protocols, (iii) approaches to extract new protocols, based on the literature, learned from biomedical and clinical datasets, and from international data challenges in neuroimaging. Dyliss contribution consists in designing a semi-automated dataset FAIRification method that will extend low-level metadata by higher level descriptions inferred from the workflow specification and execution. These descriptions will provide a summary focusing on the “what" rather than the “how", that will be instrumental to workflow recommendation as well as improved reusability of data analysis results. To this end, we will leverage domain-specific knowledge associated to biomedical datasets, as well as fine-grained workflow execution provenance traces so that data analysis results can be more easily understood, explained and shared, in line with critical open and reproducible sciences initiatives. The PhD of Ulysse Le Clanche is co-supervized by Olivier Dameron at Dyliss and Alban Gaignard at Institut du Thorax, INSERM and Univ. Nantes. 2023–2026. Dyliss grant: 185k€.

DeepImpact : Deciphering plant-microbiome interactions to enhance crop defense to bioagressors

Participants: Samuel Blanquart, Olivier Dameron, Jeanne Got, Victor Mataigne, Pauline Giraud, Anne Siegel.

DEEP IMPACT is a multidisciplinary consortium-based project that aims at combining ecology, biology, plant genetics and mathematics to identify, characterize and validate the microbial communities, plant communities and abiotic factors (including agricultural managements) explaining variation in Brassica napus and Triticum aestivum resistance to several pests. For this, we will start from an in situ approach by characterizing 100 fields (50 for each crop species) for both habitat (climatic and edaphic variables) and biotic (microbiota, virome, weed communities, pest attacks and pathobiota prevalence) features. Information from this broad characterization will be integrated into sparse and correlative statistical models to describe the relative part of the variance explained by both habitat and biotic features and correlated with a reduction of pest's attacks. This analysis will allow us to identify a combination of microbial species and soils, correlated with an increase of crop's resistance to pests. These microbial consortia will be isolated by taking advantages of newly developed culturomics methods and characterized by both whole genome sequencing and biochemical assays. Synthetic Consortia (SynComs) will be reconstructed to test their efficacy on a broad range of pests attacking both crops. 2021–2026. Dyliss grant: 176k€.

SEABIOZ : Potential microbial origins of the biostimulant properties of extracts from a brown algae holobinte

Participants: Samuel Blanquart, Olivier Dameron, Jeanne Got, Anne Siegel.

For sustainable agriculture, new bio-based solutions include biocontrol and the use of plant biostimulants such as aqueous seaweed extracts. The most widely exploited biomass for biostimulant production is the brown seaweed Ascophyllum nodosum and its commercial extracts, including products from the Roullier Group, have demonstrated their ability to improve plant growth and mitigate certain abiotic and biotic stresses. A unique feature of the alga is its mutualistic association with the fungal endophyte Mycophycias ascophylli and other microbes constituting an holobiont. Many questions remain as to the nature and origin of the active compounds in algal extracts. Are these bioactive metabolites produced by the host or by its microbiota? The main objective of SEABIOZ is to answer these questions by combining a multi-omics approach and systems biology. 2021–2024. Dyliss grant: 120k€.

ENDOVIRE (ANR)

Participants: Emmanuelle Becker, Olivier Dameron, Yael Tirlet.

The whole ANR project gathers 4 partners : the BIPAA platform (INRAe), the DGIMI laboratory, the BF2I laboratory and the Dyliss team of IRISA. The project is focused about the understanding of how genes of a endogeneized viral genome in a parasitoid wasp are the activated and regulated. The available data produced by the consortium will cover genomics, epigenomics, pathways, regulation and orthology. We will contribute to identify the key actors involved in the activation of parasitoids genes, to propose a data and knowledge integration framework for the data of the global project, and to develop integrative data analysis methods for elucidating the mechanism involving the key actors identified in the first point. It will consist in proposing a library of queries (which contains a reasoning part), and further to propose regulation mechanisms based on heterogeneous -omics data across interacting organisms. To tackle the different challenges, our appoach will be based on (1) adequate statistical analysis workflows or methods, (2) Semantic Web technologies and AskOmics developed within the team, (3) knowledge-guided traversal strategies across multiplex graphs. 2023–2025. Total grant: 630k€. Dyliss grant: 176k€.

10.4.1 Programs funded by Inria

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Journées Algométabionte, 3rd March 2024, Roscoff. Some perspectives on the enzymatic annotation of the dark proteome using deep protein language models [François Coste ]
• Journées CNRS MERIT: Usage de l'IA pour l'annotation fonctionnelle et structurale des génomes, 7th Nov 2024, Paris. Enzymatic annotation of protein sequences with a deep language model [François Coste ]
• École d'été interdisciplinaire en numérique de la santé (EINS 2024), 03–07th June 2024 Univ. Sherbrooke Canada. Intégration et interrogation avancées de données et de connaissances grâce au Web Sémantique [Olivier Dameron ]
• École thématique sur la Bioinformatique Intégrative, Institut Français de Bioinformatique (ETBII, 25th–29th March 2024, Fréjus) : animateur du thème Web Sémantique [Olivier Dameron ]
• Congreso futuro 2024, organized by the Chilean congress at santiago de Chile, january 2024 Symbiosis and metabolism [Anne Siegel ]
• Metabolic club workshop, university of chile, january 2024 Study and Comparisons of metabolic maps : adapt to the triptych of knowledge – data – scaling up [Anne Siegel ]
• 5 minutes du centre Henri LebesgueSymbiose, biologie des systèmes, et discrétisation de systèmes dynamiques, février 2024 [Anne Siegel ]

11.1.5 Leadership within the scientific community

11.1.6 Scientific expertise

Evaluation of international projects

• COST action 2024 [Anne Siegel ]

Evaluation of national projects

• ELIXIR-FR [Olivier Dameron ]

11.1.7 Research administration

11.2.1 Teaching

• Master : Emmanuelle Becker , "R, Data, and Visualisation", 61h, Master 1 in Bioinformatics, Master 1 in Ecology and Environment, Univ. Rennes, France
• Master : Emmanuelle Becker , "Object oriented programming", 39h, Master in Bioinformatics, Univ. Rennes, France
• Master : Emmanuelle Becker , "Method", 15h, Master 2 in Computer Sciences, Univ. Rennes, France
• Master : Emmanuelle Becker , "Biological networks", 31h, Master 2 in Bioinformatics, Univ. Rennes, France
• Master : Emmanuelle Becker , "Introduction to Bioinformatics", 3h, Master MEEF Biology, Univ. Rennes, France
• Master : Emmanuelle Becker , "Manipulate and Visualize Data", 27h, Bioinformatics Minor for Master Students, Univ. Rennes, France
• Licence : Emmanuelle Becker , member of the Parcoursup jury for the Life Science Licence, Univ. Rennes, France
• Licence: Catherine Belleannée , "Formal Languages", 20h, L3 informatique, Univ. Rennes, France
• Licence: Catherine Belleannée , "Projet professionnel et communication", 16h, L1 informatique, Univ. Rennes, France
• Licence: Catherine Belleannée , "Projet professionnel et communication", 12h, L2 informatique, Univ. Rennes, France
• Licence: Catherine Belleannée , Spécialité informatique, "Functional and immutable programming", 44h, L1 mathématiques, Univ. Rennes, France
• Master: Catherine Belleannée , "Answer Set Programming", 15h, M1 informatique, Univ. Rennes, France
• Master: Catherine Belleannée , "Programmation logique et contraintes", 32h, M1 informatique, Univ. Rennes, France
• Licence: Catherine Belleannée , "Outils formels pour l'informatique", 46h, L2 informatique, Univ. Rennes, France
• Licence: Catherine Belleannée , "Fondements mathématiques", 49h, L1 informatique, Univ. Rennes, France
• Licence : Cécile Beust , "Informatique", 16h, Licence 1 PCSTM, Univ. Rennes, France
• Licence : Cécile Beust , "Data : Sciences des données", 24h, Licence 2 ISTN, ISTIC, France
• Master : Cécile Beust , "Apprentissage Statistique", 27h, Master 1 Molecular and Cellular Biology, Univ. Rennes, France
• Master : Cécile Beust , "Data Engineering in Life Sciences", 5h, Master 2 Bioinformatics, Univ. Rennes, France
• Master : Myriam Bontonou , "Optimisation", 10.5h, Master 1 Info (IA), ISTIC, Univ. Rennes, France
• Master : Myriam Bontonou , "Modelisation and Discrete Optimisation", 30h, Master 1 MIAGE, ISTIC, Univ. Rennes, France
• Licence : Myriam Bontonou , "Graph Modeling and Algorithms", 21h, Licence 3 Info and MIAGE, ISTIC, Univ. Rennes, France
• Licence : Myriam Bontonou , "Introduction to AI", 27h, Licence 1, SVE, Univ. Rennes, France
• Licence : Myriam Bontonou , "Databases", 6h, Licence 3 MIAGE, ISTIC, Univ. Rennes, France
• Master : Océane Carpentier , "Data Engineering in Life Sciences", 5h, Master 2 Bioinformatics, Univ. Rennes, France
• Licence: Olivier Dameron , "Programmation 1", 98h, Licence 1 informatique, Univ. Rennes, France
• Licence: Olivier Dameron , "Complément informatique", 24h, Licence 1 informatique, Univ. Rennes, France
• Licence: Olivier Dameron , "Introduction à l'IA", 3h, Licence 1 sciences de la vie et de l'environnement, Univ. Rennes, France
• Licence: Olivier Dameron , "Data analysis and statistics", 24h, Licence 2 informatique, Univ. Rennes, France
• Licence: Olivier Dameron , "Graph Modeling and Algorithms", 21h, Licence 2 informatique, Univ. Rennes, France
• Licence: Olivier Dameron , "Programmation avancée", 36h, Licence 3 miage, Univ. Rennes, France
• Master: Olivier Dameron , "Semantic Web", 10h, Master 1 miage, Univ. Rennes, France
• Master: Olivier Dameron , "Data Engineering in Life Science", 20h, Master 2 in bioinformatics, Univ. Rennes, France
• Master: Olivier Dameron , "Internship", 28h, Master 2 in bioinformatics, Univ. Rennes, France
• Licence: Pablo Espana Gutierrez , "Langages Formels et Calculabilité", 20h, L3SIF, ENS Rennes, France
• Licence: Pablo Espana Gutierrez , "Remise à niveau MPI", 20h, L3SIF, ENS Rennes, France
• Licence: Pablo Espana Gutierrez , "Préparation à l'agrégation", 8h, ENS Rennes, France
• Master : Juliette Francis , "Apprentissage Statistique", 30h, Master 1 in Bioinfortmatics, Univ. Rennes, France
• Licence : Yann Le Cunff "Modélisation des phénomènes du vivant", 30h, L2 Biologie, Univ. Rennes, France
• Master: Yann Le Cunff , "Apprentissage statistique", 110h, Master 1 in Bioinfortmatics Univ. Rennes, France
• Master: Yann Le Cunff , "Biologie aux interfaces", 25h, Master 1 in Biology, Univ. Rennes, France
• Master: Yann Le Cunff ,"Simulating dynamic systems in biology", 20h, Master 2 in bioinformatics, Univ. Rennes, France
• Master: Yann Le Cunff , "Applied Interdisciplinarity", 20h, Master 2 in biology, Univ. Rennes, France
• PhD program: Yann Le Cunff , "Introduction to Machine Learning", 20h, FdV PhD Program, Sorbonne Paris Université, Paris, France
• Master : Corentin Lucas , "Apprentissage Statistique", 27h, Master 1 Molecular and Cellular Biology, Univ. Rennes, France
• Master : Corentin Lucas , "Data Engineering in Life Sciences (DEL)", 5h, Master 2 Bioinformatics, Univ. Rennes, France
• Master : Victor Mataigne , "Programming in R", 18h, Master 1 in Natural Resources and Biodiversity, Univ. Rennes, France
• Master : Victor Mataigne , "Introduction to Bioinformatics", 8.5h, Master in Biology and Agrosciences, Institut Agro Rennes Angers, France
• Master : Yael Tirlet , "Short Introduction to R (SIR) / Programming with R (PAR)", 18h, Master 1 in Bioinformatics, Master 1 in Ecology and Environment, Univ. Rennes, France
• Master : Yael Tirlet , "Data Engineering in Life Sciences (DEL)", 5h, Master 2 Bioinformatics, Univ. Rennes, France
• Licence : Yael Tirlet , "Data : Science des Données (DSD)", 24h, Licence 2 Informatique, Istic, Rennes, France
• Licence : Yael Tirlet , "Projet Personnel et Professionnel de l'étudiant (3PE)", 9h, Licence 1 informatique, Istic, Rennes, France

11.2.2 Supervision

PhD thesis

• PhD in progress: Moana Aulagner , Modeling microbiota interactions in plants to build synthetic microbial communities for enhanced biocontrol and biostimulation, started in Oct 2023, supervized by Samuel Blanquart , Anne Siegel and C. Bartoli-Kautski (INRAe IGEPP)
• PhD in progress: Moussa Baddour, Extraction de phénotypes à partir de comptes-rendus médicaux textuels et mise en relation avec le génotype, started in May 2023, supervized by Olivier Dameron , M. De Tayrac (Rennes Hospital), S. Paquelet (b<>com) and T. Labbé (Orange)
• PhD in progress: Cécile Beust , Knowledge-guided rules for generating context-specific views on a knowledge graph: application to biological networks, started in Oct 2023, supervized by Emmanuelle Becker , Olivier Dameron and Nathalie Théret
• PhD in progress: Océane Carpentier , Integrating prior knowledge for a better patient representation, started September 2024, supervized by Emmanuelle Becker , Yann Le Cunff , A. Bannay and N. Jay (LORIA)
• PhD in progress: Elisa Chenel , Study of protein co-evolution to identify interaction regions involved in TGFbeta growth factor activation, started in Oct 2024, supervized by Samuel Blanquart , François Coste and N. Nathalie Théret
• PhD in progress: Pablo Espana Gutierrez , Learning models with explicit dependencies between residues to predict protein functions, started in September 2023, supervized by François Coste and Olivier Dameron
• PhD in progress: Juliette Francis , Intégration de données hétérogènes pour la prédiction de phénotype, started in October 2024, supervized by Yann Le Cunff and M. Mariadasssou (INRAe)
• PhD in progress: Pauline Giraud , Méthodes hybrides pour l'inférence ab-initio de voies métaboliques chez des eucaryotes marins, started in November 2024, supervized by Anne Siegel and G. Markov (CNRS, Station biologique de Roscoff)
• PhD in progress: Ulysse Le Clanche , Knowledge-driven dataset FAIRification: from workflow runs to domain-specific annotations, started in October 2024, supervized by Olivier Dameron and A. Gaignard (CNRS, Institut du Thorax INSERM Nantes)
• PhD in progress: Corentin Lucas , Integration of multi-modal data for longitudinal follow-up of Crohn's disease patients, started in Oct 2023, supervized by Emmanuelle Becker , Yann Le Cunff
• PhD: Baptiste Ruiz , Algorithmes d'apprentissage automatique appliqués au microbiote Intégration de connaissances a priori pour de meilleures prédictions de phénotype, started in Oct 2021, defended in Nov 2024, supervized by Yann Le Cunff , Anne Siegel
• PhD: Kerian Thuillier , Inférence de règles booléennes contrôlant des modèles hybrides de systèmes biologiques multi-échelles, started in Oct 2021, defended in Sep 2024, supervized by Anne Siegel and L. Paulevé (LABRI)
• PhD in progress: Yael Tirlet , Integrative method for multi-omics data analysis with application to the activation and regulation of an endogeneized viral genome in a parasitoid wasp, started in Oct 2023, supervized by Emmanuelle Becker , Olivier Dameron and F. Legeai (INRAe)

Internship

• M2 intership: Elisa Chenel , Recherche de motifs fonctionnels dans une famille de protéines multi-domaines : les Fibulines. Jan-Jul 2024, supervized by Samuel Blanquart and Nathalie Théret .
• L3 internship: Eoghan Chevé , Distance-based amino acid conservation score, Jun-Jul 2024, supervized by Pablo Espana Gutierrez and François Coste
• M2 internship : Juliette Francis , Intégration de données hétérogènes pour la prédiction de phénotype, Jan-Jul 2024, supervized by Yann Le Cunff and M. Mariadasssou (INRAe)
• M2 intership: Hugo Mingarelli , Enrichir MetaCyc avec des connaissances sur les molécules pour l'analyse de réseaux de réactions chimiques. Jan-Jul 2024, supervized by Olivier Dameron et Jeanne Got .
• M1 Internship : Noryah Safla , Prédiction de phénotype de croissance d'algue à partir de données de microbiote, Apr-May 2024, supervized by Yann Le Cunff and S.Dittami (Roscoff)

11.2.3 Doctoral advisory committees (CSID)

• Maria-Mafalda Almeida, Univ. Rennes [Emmanuelle Becker ]
• Juan Andrés Cisneros–Jacome, Univ. Rennes [Emmanuelle Becker ]
• Maëlys Auffret, Univ. Rennes 2 [Emmanuelle Becker ]
• Yvon Awuklu, Univ. Bordeaux [Olivier Dameron ]
• Clément Bousquet, Univ. de Rennes [François Coste ]
• Simon Brocard, Nantes Université [François Coste ]
• Andrea Checcoli, Institut Curie [Anne Siegel ]
• Dorian Chenet, Univ. Rennes [Samuel Blanquart ]
• Guénolé Dande, Univ. de Rennes [Olivier Dameron ]
• Guillaume Doré, Univ. Rennes [Emmanuelle Becker ]
• Jin-Mei Gao, Université Paris-Saclay [Emmanuelle Becker ]
• Silvia Grosso, INSA Lyon [Yann Le Cunff ]
• Mats Kohler–Dijkstra, Univ. Rennes [Emmanuelle Becker ]
• Gabriel Mastrilli, Univ. de Rennes [François Coste ]
• Meije Mathé, Univ. Toulouse [Olivier Dameron ]
• Thibaut Peyric, Univ. Lyon [Yann Le Cunff ]
• Quentin Vacher, Univ. Rennes [Emmanuelle Becker ]
• Maelle Zonnequin, Sorbonne Université [Anne Siegel ]

11.2.4 Juries

Referee of PhD thesis

• Elsa Claude, Université Laval and Université de Bordeaux [Emmanuelle Becker , president, François Coste ]
• Louise Dupuis, Sorbonne University [Emmanuelle Becker ]
• Danilo Dursoniah, Lille university [Anne Siegel ]
• Romane Junker, Paris-Saclay University [Emmanuelle Becker ]
• Hasna Maayouf, Université de Haute Alsace, Université de Strasbourg [Nathalie Théret ]
• Alix Simon, Strasbourg University [Emmanuelle Becker ]
• Kerian Thuillier, Univ. Rennes [Emmanuelle Becker , president]

Member of PhD thesis juries

• Morgane Lallier, Université de Nantes [Nathalie Théret , president]
• Raissa Silva, Université de Montpellier [Yann Le Cunff ]

Member of habilitation thesis juries

• Carito Guziolowski, Univ. Nantes [Anne Siegel , president]
• Emeline Roux, Univ. Rennes [Emmanuelle Becker , president]

11.3.1 Participation in Live events

• Invited talk at Conférence Culture Générale Bio-info, Masters 1 et 2 Bioinformatique, 15th Nov 2024, Rennes. Linguistic modelling of protein sequences [François Coste ]
• Intervention in the research initiation workshop Réunion des Jeunes Mathématiciennes et Informaticiennes (RJMI), 12 Jan - 14 Jan 2024 in Bruz (Rennes). [Pablo Espana Gutierrez ]
• Invited talk at the graduation ceremony of the computer science department of ENS Rennes, 17th May 2024 in Bruz (Rennes). Learning models with explicit dependencies between residues to predict protein functions [Pablo Espana Gutierrez ]
• Intervention and organization of the computer science research initiation week CAMP FACTO, 21 Oct - 25 Oct 2024 in Saint-Chamond [Pablo Espana Gutierrez ]
• Invited talk by JeBIF association for Masters 1 and 2 Bioinformatique, 29th Nov 2024 in Rennes. careers in bioinformatics [Jeanne Got ]
• Portraits de femmes engagées, ministère éducation nationale mars 2024 [Anne Siegel ]
• Teaching computer science and the Python language to middle-school girls through creative programming, Action LCLC (elles codent, elles créent), May and Jun 2024, Collège le Landry, Rennes, France [Yael Tirlet ]

International journals

• 12 articleC.Cécile Beust, E.Emmanuelle Becker, N.Nathalie Théret and O.Olivier Dameron. BioPAX in 2024: Where we are and where we are heading.Computational and Structural Biotechnology Journal23November 2024, 3999-4010HALDOIback to text
• 13 articleM.Matthieu Bouguéon, V.Vincent Legagneux, O.Octave Hazard, J.Jeremy Bomo, A.Anne Siegel, J.Jerome Feret and N.Nathalie Théret. A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression.PLoS Computational Biology2072024, e1011858HALDOIback to text
• 14 articleF.France Denoeud, O.Olivier Godfroy, C.Corinne Cruaud, S.Svenja Heesch, Z.Zofia Nehr, N.Nachida Tadrent, A.Arnaud Couloux, L.Loraine Brillet-Guéguen, L.Ludovic Delage, D.Dean Mckeown, T.Taizo Motomura, D.Duncan Sussfeld, X.Xiao Fan, L.Lisa Mazéas, N.Nicolas Terrapon, J.Josué Barrera-Redondo, R.Romy Petroll, L.Lauric Reynes, S.-W.Seok-Wan Choi, J.Jihoon Jo, K.Kavitha Uthanumallian, K.Kenny Bogaert, C.Céline Duc, P.Pélagie Ratchinski, A.Agnieszka Lipinska, B.Benjamin Noel, E.Eleanor Murphy, M.Martin Lohr, A.Ananya Khatei, P.Pauline Hamon-Giraud, C.Christophe Vieira, K.Komlan Avia, S. S.Svea Sanja Akerfors, S.Shingo Akita, Y.Yacine Badis, T.Tristan Barbeyron, A.Arnaud Belcour, W.Wahiba Berrabah, S.Samuel Blanquart, A.Ahlem Bouguerba-Collin, T.Trevor Bringloe, R. A.Rose Ann Cattolico, A.Alexandre Cormier, H.Helena Cruz de Carvalho, R.Romain Dallet, O.Olivier de Clerck, A.Ahmed Debit, E.Erwan Denis, C.Christophe Destombe, E.Erica Dinatale, S.Simon Dittami, E.Elodie Drula, S.Sylvain Faugeron, J.Jeanne Got, L.Louis Graf, A.Agnès Groisillier, M.-L.Marie-Laure Guillemin, L.Lars Harms, W. J.William John Hatchett, B.Bernard Henrissat, G.Galice Hoarau, C.Chloé Jollivet, A.Alexander Jueterbock, E.Ehsan Kayal, A.Andrew Knoll, K.Kazuhiro Kogame, A.Arthur Le Bars, C.Catherine Leblanc, L.Line Le Gall, R.Ronja Ley, X.Xi Liu, S.Steven Loduca, P. J.Pascal Jean Lopez, P.Philippe Lopez, E.Eric Manirakiza, K.Karine Massau, S.Stéphane Mauger, L.Laetitia Mest, G.Gurvan Michel, C.Catia Monteiro, C.Chikako Nagasato, D.Delphine Nègre, E.Eric Pelletier, N.Naomi Phillips, P.Philippe Potin, S.Stefan Rensing, E.Ellyn Rousselot, S.Sylvie Rousvoal, D.Declan Schroeder, D.Delphine Scornet, A.Anne Siegel, L.Leila Tirichine, T.Thierry Tonon, K.Klaus Valentin, H.Heroen Verbruggen, F.Florian Weinberger, G.Glen Wheeler, H.Hiroshi Kawai, A.Akira Peters, H. S.Hwan Su Yoon, C.Cécile Hervé, N.Naihao Ye, E.Eric Bapteste, M.Myriam Valero, G.Gabriel Markov, E.Erwan Corre, S.Susana Coelho, P.Patrick Wincker, J.-M.Jean-Marc Aury and J. M.J. Mark Cock. Evolutionary genomics of the emergence of brown algae as key components of coastal ecosystems.Cell2024. In press. HALDOIback to textback to text
• 15 articleM.Marine Louarn, G.Guillaume Collet, E.Eve Barre, T.Thierry Fest, O.Olivier Dameron, A.Anne Siegel and F.Fabrice Chatonnet. Regulus infers signed regulatory relations from few samples&#039; information using discretization and likelihood constraints.PLoS Computational Biology2012024, e1011816HALDOIback to text
• 16 articleB.Baptiste Ruiz, A.Arnaud Belcour, S.Samuel Blanquart, S.Sylvie Buffet-Bataillon, I. L.Isabelle Luron Le Huërou-Luron, A.Anne Siegel and Y.Yann Le Cunff. SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.PLoS Computational Biology20112024, e1012577HALDOIback to text
• 17 articleY.Yael Tirlet, M.Matéo Boudet, E.Emmanuelle Becker, F.Fabrice Legeai and O.Olivier Dameron. Generic and queryable data integration schema for transcriptomics and epigenomics studies.Computational and Structural Biotechnology Journal23December 2024, 4232-4241HALDOIback to textback to text
• 18 articleM.Maëlle Zonnequin, A.Arnaud Belcour, L.Ludovic Delage, A.Anne Siegel, S.Samuel Blanquart, C.Catherine Leblanc and G. V.Gabriel V Markov. Empirical evidence for metabolic drift in plant and algal lipid biosynthesis pathways.Frontiers in Plant Science152024, 1339132HALDOIback to textback to text

International peer-reviewed conferences

• 19 inproceedingsM.Moussa Baddour, S.Stéphane Paquelet, P.Paul Rollier, M. D.Marie De Tayrac, O.Olivier Dameron and T.Thomas Labbé. Phenotypes Extraction from Text: Analysis and Perspective in the LLM Era.IS 2024 - 12th IEEE International Conference on Intelligent SystemsVarna, Bulgaria2024, 1-8HALDOIback to text
• 20 inproceedingsK.Kerian Thuillier, A.Anne Siegel and L.Loïc Paulevé. CEGAR-Based Approach for Solving Combinatorial Optimization Modulo Quantified Linear Arithmetics Problems.AAAI 2024 - The 38th Annual AAAI Conference on Artificial IntelligenceVancouver, Canada2024, 1-8HALback to text

Doctoral dissertations and habilitation theses

• 21 thesisB.Baptiste Ruiz. Machine Learning Algorithms in the health sector : integration of functional knowledge to enhance the analysis of gut microbiota data.INRIA Rennes - Bretagne Atlantique; INRAENovember 2024HALback to text
• 22 thesisB.Baptiste Ruiz. Machine learning algorithms in the health sector : integration of functional knowledge toenhance the analysis of gut microbiota data.Université de RennesNovember 2024HAL
• 23 thesisK.Kerian Thuillier. Hybrid satisfiability methods for the inference of boolean regulations controlling metabolic networks.Université de RennesSeptember 2024HALback to text

Reports & preprints

• 24 miscC. M.Constanza M Andreani-Gerard, N. E.Natalia E Jiménez, R.Ricardo Palma, C.Coralie Muller, P.Pauline Hamon-Giraud, Y.Yann Le Cunff, V.Verónica Cambiazo, M.Mauricio González, A.Anne Siegel, C.Clémence Frioux and A.Alejandro Maass. Modeling the emergent metabolic potential of soil microbiomes in Atacama landscapes.2024HALDOIback to textback to text
• 25 miscA.Arnaud Belcour, P.Pauline Hamon-Giraud, A.Alice Mataigne, B.Baptiste Ruiz, Y. L.Yann Le Cunff, J.Jeanne Got, L.Lorraine Awhangbo, M.Megane Lebreton, C.Clémence Frioux, S.Simon Dittami, P.P. Dabert, A.Anne Siegel and S.Samuel Blanquart. Estimating consensus proteomes and metabolic functions from taxonomic affiliations.2025HALDOI
• 26 miscF.François Moreews, J.-B.Jean-Baptiste Bougaud, E.Emmanuelle Becker, F.Florence Gondret and O.Olivier Dameron. BioPAX-Explorer: a Python Object-Oriented framework for overcoming the complexity of querying biological networks.September 2024HALDOIback to text

Other scientific publications

• 27 inproceedingsJ.Juliette Audemard, S.Sébastien Halary, G.Gabriel Markov, J.Jeanne Got, A.Anne Siegel, M.Marie Lefebvre, J.Julie Leloup, B.Benjamin Marie, N.Nicolas Creusot, B.Binta Dieme and C.Cleḿence Frioux. Metagenome-scale metabolic modelling for the characterization of cross-feeding interactions in Microcystis-associated microbial communities, in the context of freshwater cyanobacterial blooms.JOBIM 2024 - Journées Ouvertes en Biologie, Informatique et MathématiquesToulouse, France2024, 1-1HALback to textback to text
• 28 thesisE.Elisa Chenel. Searching for functional motifs in a family of multi-domain proteins: Fibulins.Rennes 1RennesJuly 2024, 38HALback to text
• 29 thesisE.Eoghan Chevé. Distance-based amino acid conservation score.Ens RennesJuly 2024HALback to text