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  51. 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  86, 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 there 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 9, 8. 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  66. 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  78.

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  57 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  83.

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  82, 81.

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). It also generates outputs to explore the resulting networks with Askomics. It has been used for reconstructing metabolic networks of micro and macro-algae  76, extremophile bacteria  60 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). 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)  53. 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  79.

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 10, multi-layer reactions in interaction graphs  54, and multi-layer information in large-scale Petri nets  49. 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 10. 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  49. 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).
• News of the Year:
  2022: (1) Correction of various bugs, improvement of the user interface, and optimizations. (2) Modification of the description of relations and attributes are described. Currently blank nodes are used to avoid "phantom" relations and attributes. (3) Adding a management of ontologies and prefixes (possibilities of loading a part of NCBITAXON, for example going to search all descendants of a class). (4) Adding a "single tenant" mode. All graphs are public by default. Query performances have been significantly improved.
• URL:
  https://­askomics.­org/
• Authors:
  Charles Bettembourg, Xavier Garnier, Anthony Bretaudeau, Fabrice Legeai, Olivier Dameron, Olivier Filangi, Yvanne Chaussin, Mateo Boudet
• Contact:
  Olivier Dameron
• Partners:
  Université de Rennes 1, CNRS, INRA

7.1.2 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.
• News of the Year:
  2022: (1) Fix the number of colors used to color the taxon in the powergraph.(2) Change the shapes of the nodes in the html output of m2m_analysis: circle for essential symbionts and square for alternative symbionts. (3) Release of version 1.5.3.
• URL:
  https://­github.­com/­AuReMe/­metage2metabo
• Publication:
  hal-02395024
• Contact:
  Clemence Frioux
• Participants:
  Clemence Frioux, Arnaud Belcour, Anne Siegel

7.1.3 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:
  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.
• URL:
  http://­cadbiom.­genouest.­org
• Publications:
  inserm-00978313, hal-01242893, hal-01559249, hal-03693653
• Contact:
  Anne Siegel
• Participants:
  Geoffroy Andrieux, Michel Le Borgne, Nathalie Theret, Nolwenn Le Meur, Pierre Vignet, Anne Siegel

7.1.4 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. Supporting 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.
• News of the Year:

  The code of Pax2graphml has been refactored and extended for including new reaction graph manipulation features. We have also recoded the RDF import module. New compatible datasets have been generated from 17 BIOPAX data sources. A landing page and a demo jupyter notebook and documentation have been created.

  The article "PAX2GRAPHML: a Python library for large-scale regulation network analysis using BIOPAX" was published in Bioinformatics (https://hal.archives-ouvertes.fr/hal-03265223)

• URL:
  https://­pax2graphml.­genouest.­org/
• Publication:
  hal-03265223
• Contact:
  François Moreews
• Partner:
  INRAE

7.1.5 Protomata

• Keywords:
  Proteins, Machine learning, Pattern discovery, Grammatical Inference, Bioinformatics
• Scientific Description:
  Inference of automata modelling protein sequences by partial local alignment
• Functional Description:

  This tool is a grammatical inference framework suitable for learning the specific signature of a functional protein family from unaligned sequences by partial and local multiple alignment and automata modelling. It performs a syntactic characterization of proteins by identification of conservation blocks on sequence subsets and modelling of their succession. Possible fields of application are new members discovery or study (for instance, for site-directed mutagenesis) of, possibly non-homologous, functional families and subfamilies such as enzymatic, signalling or transporting proteins.

  Given a sample of sequences belonging to a structural or functional family of proteins, Protomata-Learner infers an automaton characterizing the family by partial local alignment of the sequences. Automata are graphical models representing a (potentially infinite) set of sequences. Able to express alternative local dependencies between the positions, automata offer a finer level of expressivity than classical sequence patterns (such as PSSM, Profile HMM, or Prosite Patterns) and can model more than homologous sequences. They are well suited to get new insights into a family or to search for new family members in the sequence data banks, especially when approaches based on classical multiple sequence alignments are insufficient.

  The three main modules integrated in the Protomata-learner workflow are available as well as stand-alone programs: 1) paloma builds partial local multiple alignments, 2) protobuild infers automata from these alignements and 3) protomatch and protoalign scans, parses and aligns new sequences with learnt automata. The suite is completed by tools to handle or visualize data and can be used online by the biologists via a web interface on Genouest Platform.

• News of the Year:
  Final release of paloma-2 (new and faster version of paloma in modern C++) introducing a new format for partial local alignments.
• URL:
  http://­tools.­genouest.­org/­tools/­protomata/
• Contact:
  François Coste
• Participant:
  François Coste
• Partners:
  Université de Rennes 1, CNRS, Inria

7.1.6 PPsuite

• Keywords:
  Proteins, Sequence alignment, Bioinformatics, Machine learning, Homology search
• Scientific Description:
  Comparison of protein sequences using coevolutionary dependencies between residues.
• Functional Description:
  This suite contains the following tools : - MakePotts infers a Potts model from a sequence or a multiple sequence alignment - PPalign aligns Potts models and corresponding sequences - VizPotts allows to visualize inferred Potts models and VizContacts allows to visualize inferred couplings with respect to actual contacts in a 3D protein structure.
• News of the Year:
  A new Potts model inference method was developed, replacing the call to the external package CCMpredPy. This method is considerably faster, makes it possible to represent deeper and longer multiple sequence alignments, and is less sensitive to small sampling variations. To better handle insertion and deletions events, trimmed positions can now be represented by zero insertion penalties, enabling PPalign to return full alignment of the sequences, and the offset hyperparameter is now automatically determined from pseudocount rate and background amino acid frequencies.
• URL:
  https://­www-dyliss.­irisa.­fr/­ppalign/
• Publications:
  hal-02402646, hal-02862213, hal-03264248, hal-03926272
• Contact:
  François Coste
• Participants:
  François Coste, Hugo Talibart, Mathilde Carpentier

7.1.7 Transformer Framework for Protein Characterization

• 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. 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.
• News of the Year:
  We added the ability to obtain attention maps during inference and combine these to get residue importance scores. We included in the framework eight additional interpretability methods that provide residue importance scores from the literature: Attention last layer, LIME, GradCam, LRP, Rollout, Gradients, InputXGrad, and Integrated gradient. And we implemented the computation of metrics enabling to evaluate the residue importance scores with respect to a ground truth target: Precision, Recall, Average Precision, Recall Gain, Precision gain, Maximum f-gain, Precision Recall Gain Area Under the Curve.
• URL:
  https://­gitlab.­inria.­fr/­nbuton/­tfpc
• Contact:
  Nicolas Buton
• Participants:
  Nicolas Buton, François Coste, Yann Le Cunff

7.1.8 Emapper2GBK

• Keywords:
  Bioinformatics, Metabolic networks, Functional annotation
• Functional Description:
  Starting from FASTA and Eggnog-mapper annotation files, Emapper2GBK builds a GBK file that is suitable for metabolic network reconstruction with Pathway Tools, and adds the GO terms and EC numbers annotations in the GenBank file.
• News of the Year:
  2022: Using gffutils region to speed up emapper2gbk. Supporting for gmove and eggnog GFF format. Adding gff-type option: add mRNA and gene parameters. Allowing the tool to use the IDs in "mRNA" or "gene" field in the gff to match the faa file IDs. Improving error messages and updating the readme.
• URL:
  https://­github.­com/­AuReMe/­emapper2gbk
• Publication:
  hal-02395024
• Contact:
  Clemence Frioux
• Participants:
  Clemence Frioux, Arnaud Belcour, Anne Siegel

7.1.9 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.
• News of the Year:
  2022: Transforming command merge into spontaneous. Adding two filtering options: –filtering –union and –filtering –intersection in the orthology step. Improving Pypi and readme file.
• URL:
  https://­github.­com/­AuReMe/­aucome
• 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

7.1.10 mpwt

• Keywords:
  Metabolic networks, Multi-processor
• Functional Description:
  mpwt is a Python package for running Pathway Tools on multiple genomes using multiprocessing. More precisely, it launches one PathoLogic process for each organism. This allows to increase the speed of draft metabolic network reconstruction when working on multiple organisms.
• News of the Year:
  2022: Changing mpwt to run the processes independently. Now it can take as input PathoLogic files without fasta. This should make mpwt compatible with PathoLogic files created by EsMeCaTa. Adding the -dump-flat-files-biopax option compatible with Pathway Tools 26.0.
• Publication:
  hal-02395024
• Contact:
  Anne Siegel
• Participants:
  Arnaud Belcour, Anne Siegel, Clemence Frioux, Meziane Aite

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

Participants: Anne Siegel.

9.2.1 Visits of international scientists

Participants: Anne Siegel.

9.2.2 Other european programs/initiatives

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

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

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

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

IDEALG (ANR/PIA-Biotechnology and Bioresource)

Participants: Arnaud Belcour, François Coste, Jeanne Got, Anne Siegel.

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)15. 2012–2021. 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 INRAe and the research laboratories involved in the fields of physiology, nutrition, well-being/behaviour and infectiology/immunology. 2019–2022.

9.3.1 Programs funded by Inria

Pepper (projet Émergence 2021-2022 de l’Alliance Sorbonne Université)

Participants: François Coste.

The project Pepper, coordinated by Mathilde Carpentier from ISYEB (Institut de Systématique, Évolution, Biodiversité), aims at proposing a new generation of practical tools based on Potts models for the search and alignment of homologous protein sequences. In continuation of his PhD in Dyliss, Hugo Talibart is working as a postdoc in the Muséum National d'Histoire Naturelle (under the supervision of M. Carpentier and F. Coste) to enhance PPsuite with necessary practical refinements and test its application on viral protein sequences.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• Workshops "Le RNA-seq, de la paillasse à l’analyse in-silico", PLBS UAR, Lille [S. Blanquart]
• Journée de lancement du PEPR Atlasea " Des génomes marins à la synthèse in silico et in vivo de molécules" [A. Siegel]

10.1.5 Scientific expertise

10.1.6 Research administration

10.2.1 Teaching tracks responsibilities

Participants: Emmanuelle Becker, Catherine Belleannée, Olivier Dameron, Nathalie Théret.

• Coordination of the doctoral school "Biology and Health" of University of Bretagne Loire, Rennes [N. Théret]
• Coordination of the master degree "Bioinformatics", Univ. Rennes [E. Becker, O. Dameron]
• Organization of the open day of the UFR of computer science and electronics, Univ. Rennes (journée portes ouvertes Istic) [C. Belleannée]

10.2.2 Course responsibilities

Participants: Emmanuelle Becker, Catherine Belleannée, François Coste, Olivier Dameron, Yann Le Cunff, Anne Siegel, Nathalie Théret.

• "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]
• "Imperative programming", Licence 1 informatique, Univ. Rennes 1 [O. Dameron]
• "Complément informatique 1", Licence 1 informatique, Univ. Rennes 1 [O. Dameron]
• "Atelier bioinformatique", Licence 2 informatique, 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 et pathologique", Master 2 Biologie cellulaire et Moléculaire, Univ. Rennes 1 [N. Théret]
• "Machine Learning", Master 1 in Bioinformatics [Y. le Cunff]
• "Modeling dynamic systems", Licence 2, Biology [Y. Le Cunff]
• "Simulating Biological Systems", Master 2 in Bioinformatics [Y. Le Cunff]
• "Simulation and biology interfaces", Master 1 in Biology [Y. Le Cunff]
• "Applied interdisciplinarity", Master 2 in Biology [Y. Le Cunff]
• "Introduction to Machine Learning for biology", Ph.D. Program [Y. Le Cunff]

10.2.3 Teaching

Participants: Emmanuelle Becker, Catherine Belleannée, François Coste, Olivier Dameron, Yann Le Cunff, Anne Siegel, Nathalie Théret.

• 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
• Licence: C. Belleannée, Outils formels pour l'informatique, 32h, L2 informatique, Univ. Rennes 1, France
• Master: F. Coste, Supervised machine learning, 10h, M2 Science Informatique, Univ. Rennes, 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", 24h, Licence 2 informatique, Univ. Rennes 1, France
• Licence: O. Dameron, "Databases", 24h, Licence 2 informatique, Univ. Rennes 1, France
• Licence: O. Dameron, "Programmation", 54h, Licence 3 miage, Univ. Rennes 1, France
• Master: O. Dameron, "Semantic Web", 20h, Master 1 miage, Univ. Rennes 1, France
• Master: O. Dameron, "Veille technologique", 2h, Master 2 miage, Univ. Rennes 1, France
• Master: O. Dameron, 2h, "Internship", Master 1 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, 20h, "Semantic Web and bio-ontologies", Master 2 in bioinformatics, Univ. Rennes 1, France
• Master: O. Dameron, 18h, "Internship", Master 2 in bioinformatics, Univ. Rennes 1, France
• Master: A. Siegel, Integrative and Systems biology, Master 2 in bioinformatics, Univ. Rennes 1.
• Licence : Y. Le Cunff "Modélisation des phénomènes du vivant", 30h, L2 Biologie, Univ. Rennes 1, France
• Master: Y. Le Cunff, "Apprentissage statistique", 110h, Master 1 in Bioinfortmatics Univ. Rennes 1, France
• Master: Y. Le Cunff, "Biologie aux interfaces", 25h, Master 1 in Biology, Univ. Rennes 1, France
• Master: Y. Le Cunff,"Simulating dynamic systems in biology", Master 2 in bioinformatics, 20h, Univ. Rennes 1, France
• Master: Y. Le Cunff, "Applied Interdisciplinarity", 20h, Master 2 in biology, Univ. Rennes 1, France
• PhD program: Y. Le Cunff,"Introduction to Machine Learning", 20h, FdV PhD Program, Sorbonne Paris Université, Paris, France

10.2.4 Supervision

Participants: Emmanuelle Becker, Catherine Belleannée, Samuel Blanquart, François Coste, Olivier Dameron, Jeanne Got, Yann Le Cunff, Anne Siegel, Nathalie Théret.

• PhD: Arnaud Belcour, Combining knowledge-based and sequence comparison approaches to elucidate metabolic functions, from pathways to communities, defended Oct. 21st 2022, supervised by A. Siegel and S. Blanquart. 34
• PhD: Olivier Dennler, Modular functional characterization of ADAMTL and ADAMTSL protein fami- lies, defended Dec. 19th 2022, supervised by N. Theret, F. Coste, S. Blanquart and C. Belleannée. 35
• PhD: Virgilio Kmetzsch, Multimodal analysis of neuroimaging and transcriptomic data in genetic frontotemporal dementia, defended 26th 2022, co-supervised by E. Becker and O. Colliot (Inria ARAMIS). 36
• PhD in progress: Matthieu 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: Baptiste Ruiz Algorithmes d’apprentissage automatique appliqués au microbiote : Intégration de connaissances a priori pour de meilleures prédictions de phénotype, started in Oct. 2021, supervised by Y. Le Cunff and A. Siegel
• PhD in progress: Kerian Thuillier Inférence de règles booléennes contrôlant des modèles hybrides de systèmes biologiques multi-échelles, started in Oct. 2021, supervised by A. Siegel and L. Paulevé (LABRI)
• PhD interrupted in 2022: Marc Melkonian Intégration de données et de connaissances pour l’analyse fine de l’interactome, started in Dec. 2021, supervised by E. Becker and G. Rabut (IGDR) 17, 29
• L3 ENS internship: Thibaut Antoine, Pondération d’automates obtenus par alignements partiels de séquences protéiques, May-July 2022, supervised by F. Coste 40
• M1 internship: Moana Aulagner, Draft reconstructions of metabolic networks from Nanopore and shotgun metagenomic data, Apr.– Jul. 2022, co-supervised by A. Siegel; J. Got and E. Roux (NuMeCan) 41
• M1 internship: Cécile Beust, Search for exposomic causality of liver fibrosis using network analysis, Apr.– Jul. 2022, co-supervised by N. Théret and O. Dameron 42
• M2 internship: Pauline Hamon-Giraud, Comparative analysis of genome scale metabolic networks in brown algae, Jan.–Jul. 2022, co-supervised by A. Siegel, J. Got and G. Markov (Station Biologique de Roscoff) 45
• M2 internship: Yael Tirlet, Intégration de données agricoles et environnementales à l’aide des technologies du Web sémantique, Jan.–Jul. 2022, co-supervised by M. Boudet and O. Dameron 48, 25

10.2.5 Doctoral advisory committee (CSID)

Participants: Emmanuelle Becker, François Coste, Olivier Dameron, Yann Le Cunff, Anne Siegel.

• Manon Lesage, Université de Rennes [E. Becker]
• Guillaume Doré, Université de Rennes [E. Becker]
• L. Cornanguer, Université de Rennes 1 [F. Coste]
• Yvon Awuklu, Université de Bordeaux [O. Dameron]
• Marine Djaffardjy, Université Paris-Saclay [O. Dameron]
• Tiphaine Casy, Université de Rennes 1 [O. Dameron]
• Andreas Checcoli, Institut Curie [A. Siegel]
• Maxime Lecompte, Univ. Bordeaux [A. Siegel]
• Olivier Quenez, Univ. Caen [A. Siegel]
• Indusha Kughatas, Université de Rennes 1 [Y. Le Cunff]
• Camille Kergal, Université de Rennes 1 [Y. Le Cunff]

10.2.6 Juries

Participants: Emmanuelle Becker, Catherine Belleannée, Samuel Blanquart, François Coste, Olivier Dameron, Yann Le Cunff, Anne Siegel, Nathalie Théret.

10.3.1 Articles and contents

Popularizing sciences at the national level Our team was involved in the cosupervision of a comic book gathering the portraits of 12 female computer scientists Les décodeuses du numérique. The book was sent to all French high schools and is freely available online16: more than 30,000 views and 5,000 downloads since September 2021. This led to several intervention in round-tables, large-audience conferences and media: Radio France International, Emission “autour de la question” - ONU info, Salon educatice-educatech - Round-table at the ESIR Engineer school, Rennes, “parité et les métiers du numérique au féminin”, 2021 - Women in AI conference, 2022 - Round- table Femmes dans l’informatique, Sorbonne Université - Semaine de l’égalité, Académie de Toulouse - Journée citoyenneté numérique, Academy of Créteil - Les rencontres de l’AIEF.

10.3.2 Interventions

• "Talkin’ Bout A ML Revolution", an introduction to Artificial Intelligence and Machine Learning at "Journée de l’axe Bio-informatique du réseau Biogenouest: L’intelligence Artificielle au service des sciences de la vie" [F. Coste]
• "Recherche de poste pour les jeunes bioinfos", panel JEBIF (French association of the young bioinformaticians) [O. Dameron]
• "Avenir de la bioinformatique", panel JEBIF (French association of the young bioinformaticians) [O. Dameron]

10.3.3 Contributions to open source projects

A. Belcour filled several issues and provided bugfixes to open source projects in connection with his research activity at DYLISS.

International journals

• 11 articleS.Sylvie Buffet-Bataillon, G.Guillaume Bouguen, F.François Fleury, V.Vincent Cattoir and Y.Yann Le Cunff. Gut microbiota analysis for prediction of clinical relapse in Crohn’s disease.Scientific Reports121December 2022, 19929
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• 12 articleN.Nicolas Guillaudeux, C.Catherine Belleannée and S.Samuel Blanquart. Identifying genes with conserved splicing structure and orthologous isoforms in human, mouse and dog.BMC Genomics2312022, 1-14
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• 13 articleH.Hetty Kleinjan, C.Clémence Frioux, G.Gianmaria Califano, M.Méziane Aite, E.Enora Fremy, E.Elham Karimi, E.Erwan Corre, T.Thomas Wichard, A.Anne Siegel, C.Catherine Boyen and S. M.Simon M. Dittami. Insights into the potential for mutualistic and harmful host‐microbe interactions affecting brown alga freshwater acclimation.Molecular Ecology2022
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• 14 articleV.Virgilio Kmetzsch, E.Emmanuelle Becker, D.Dario Saracino, D.Daisy Rinaldi, A.Agnes Camuzat, I.Isabelle Le Ber and O.Olivier Colliot. Disease progression score estimation from multimodal imaging and microRNA data using supervised variational autoencoders.IEEE Journal of Biomedical and Health Informatics2023, 1-12
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• 15 articleV.Virgilio Kmetzsch, M.Morwena Latouche, D.Dario Saracino, D.Daisy Rinaldi, A.Agnès Camuzat, T.Thomas Gareau, I.Isabelle Le Ber, O.Olivier Colliot and E.Emmanuelle Becker. MicroRNA signatures in genetic frontotemporal dementia and amyotrophic lateral sclerosis.Annals of Clinical and Translational Neurology2022, 1-14
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• 16 articleBest paperM.Marine Louarn, F.Fabrice Chatonnet, X.Xavier Garnier, T.Thierry Fest, A.Anne Siegel, C.Catherine Faron and O.Olivier Dameron. Improving reusability along the data life cycle: a Regulatory Circuits Case Study.Journal of Biomedical Semantics1312022, 1-11
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• 17 articleBest paperM.Marc Melkonian, C.Camille Juigné, O.Olivier Dameron, G.Gwenaël Rabut and E.Emmanuelle Becker. Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases.BioinformaticsJanuary 2022, 1-7
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• 18 articleBest paperA.Anna Niarakis, D.Dagmar Waltemath, J.James Glazier, F.Falk Schreiber, S.Sarah Keating, D.David Nickerson, C.Claudine Chaouiya, A.Anne Siegel, V.Vincent Noël, H.Henning Hermjakob, T.Tomáš Helikar, S.Sylvain Soliman and L.Laurence Calzone. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.Briefings in Bioinformatics234June 2022, 1-11
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• 19 articleF. G.Fabrice G. Petit, S. P.Soazik P. Jamin, P.-Y.Pierre-Yves Kernanec, E.Emmanuelle Becker, G.Guillaume Halet and M.Michael Primig. EXOSC10/Rrp6 is essential for the eight-cell embryo/morula transition.Developmental Biology4832022, 58-65
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• 20 articleM.Milka Popova, I.Ibrahim Fakih, E.Evelyne Forano, A.Anne Siegel, R.Rafael Muñoz-Tamayo and D.Diego Morgavi. Rumen microbial genomics: from cells to genes (and back to cells).CAB Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources2022August 2022
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• 21 articleK.Kerian Thuillier, C.Caroline Baroukh, A.Alexander Bockmayr, L.Ludovic Cottret, L.Loïc Paulevé and A.Anne Siegel. MERRIN: MEtabolic Regulation Rule INference from time series data.Bioinformatics38Supplement_22022, ii127-ii133
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• 22 articleP.Pierre Vignet, J.Jean Coquet, S.Sébastien Aubert, M.Matéo Boudet, A.Anne Siegel and N.Nathalie Théret. Discrete modeling for integration and analysis of large-scale signaling networks.PLoS Computational Biology2022
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International peer-reviewed conferences

• 23 inproceedingsO.Oumarou Abdou Arbi, A.Anne Siegel and J.Jérémie Bourdon. Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine.CARI 2022 - Colloque Africain sur la Recherche en Informatique et en Mathématiques AppliquéesYaoundé, CameroonOctober 2022, 1-12
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• 24 inproceedingsV.Virgilio Kmetzsch, E.Emmanuelle Becker, D.Dario Saracino, V.Vincent Anquetil, D.Daisy Rinaldi, A.Agnès Camuzat, T.Thomas Gareau, I.Isabelle Le Ber and O.Olivier Colliot. A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases.SPIE Medical Imaging 2022: Image Processing12032Medical Imaging 2022: Image ProcessingSan Diego, California, United StatesFebruary 2022, 376-382
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Conferences without proceedings

• 25 inproceedingsO.Olivier Dameron, Y.Yael Tirlet, M.Matéo Boudet and F.Fabrice Legeai. Intégration de données de phénotypiques, environnementales et de biodiversité à l'aide des technologies du Web Sémantique.SYSINFO-INRAe 2022 - Journées INRAE systèmes d'information pour les données agro-environnementalesClermont-Ferrand, FranceNovember 2022
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• 26 inproceedingsC.Camille Juigné, O.Olivier Dameron, F.Florence Gondret and E.Emmanuelle Becker. A method to identify target molecules and extract the corresponding graph of interactions in BioPAX.BBCC2022 - Bioinformatics and Computational Biology ConferenceVirtual, ItalyDecember 2022
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• 27 inproceedingsBest paperC.Camille Juigné, O.Olivier Dameron, F.François Moreews, F.Florence Gondret and E.Emmanuelle Becker. Detection and correction of non-conformities and redundancies in complexes of molecules in BioPAX.Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM)Rennes, FranceJuly 2022, 1-25
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• 28 inproceedingsV.Virgilio Kmetzsch, E.Emmanuelle Becker, D.Dario Saracino, V.Vincent Anquetil, D.Daisy Rinaldi, A.Agnès Camuzat, T.Thomas Gareau, I.Isabelle Le Ber and O.Olivier Colliot. Highlight on Computing disease progression scores using multimodal variational autoencoders trained with neuroimaging and microRNA data.Jobim 2022 - Journées Ouvertes en Biologie, Informatique et MathématiquesRennes, FranceJuly 2022
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• 29 inproceedingsBest paperM.Marc Melkonian, C.Camille Juigné, O.Olivier Dameron, G.Gwenaël Rabut and E.Emmanuelle Becker. Highlight on semantic web technologies are effective to remove redundancies from protein-protein interaction databases and define reproducible interactomes.Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM)38ProceedingsRennes, FranceJanuary 2022, 146
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Scientific book chapters

• 30 inbookM.Matthieu Bouguéon, P.Pierre Boutillier, J.Jérôme Feret, O.Octave Hazard and N.Nathalie Théret. The rule-based model approach. A Kappa model for hepatic stellate cells activation by TGFB1.Systems Biology Modelling and Analysis: Formal Bioinformatics Methods and ToolsWileyNovember 2022, 1-76
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• 31 inbookO.Olivier Dameron. Méthodes du Web sémantique pour l’intégration de données en sciences de la vie.Intégration de données biologiquesISTE GroupJuly 2022, 1-30
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• 32 inbookC.Clémence Frioux and A.Anne Siegel. Problèmes d'optimisation combinatoire pour l'étude du métabolisme.Approches symboliques de la modélisation et de l’analyse des systèmes biologiquesEncyclopédie SciencesISTE éditionsJuly 2022
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Doctoral dissertations and habilitation theses

• 33 thesisE.Emmanuelle Becker. From homogeneous data to heterogeneous data in systems biology.Université de Rennes 1December 2022
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• 34 thesisA.Arnaud Belcour. Combining knowledge-based and sequence comparison approaches to elucidate metabolic functions, from pathways to communities.Université de Rennes 1 (UR1), Rennes, FRA.October 2022
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• 35 thesisO.Olivier Dennler. Characterization in functional modules of ADAMTS-TSL proteins, by phylogeny approaches.Université Rennes 1December 2022
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• 36 thesisV.Virgilio Kmetzsch. Multimodal analysis of neuroimaging and transcriptomic data in genetic frontotemporal dementia.Sorbonne UniversiteSeptember 2022
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Reports & preprints

• 37 miscA.Arnaud Belcour, J.Jeanne Got, M.Méziane Aite, L.Ludovic Delage, J.Jonas Collen, C.Clémence Frioux, C.Catherine Leblanc, S.Simon Dittami, S.Samuel Blanquart, G.Gabriel Markov and A.Anne Siegel. AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes.September 2022
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• 38 miscA.Arnaud Belcour, B.Baptiste Ruiz, C.Clémence Frioux, S.Samuel Blanquart and A.Anne Siegel. EsMeCaTa: Estimating metabolic capabilities from taxonomic affiliations.2022
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• 39 miscI.Ibrahim Fakih, J.Jeanne Got, C. E.Carlos Eduardo Robles-Rodriguez, A.Anne Siegel, E.Evelyne Forano and R.Rafael Muñoz-Tamayo. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium<i>Fibrobacter succinogenes</i>S85.November 2022
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Other scientific publications

• 40 thesisT.Thibaut Antoine and F.François Coste. Pondération d'automates obtenus par alignements partiels de séquences protéiques.Ecole normale supérieure de Rennes - ENS RennesAugust 2022
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• 41 thesisM.Moana Aulagner. Draft reconstructions of metabolic networks from Nanopore and shotgun metagenomic data..Université de Rennes 1July 2022
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• 42 thesisC.Cécile Beust. Search for exposomic causality of liver fibrosis using network analysis.Université de Rennes 1, FranceJuly 2022
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• 43 inproceedingsN.Nicolas Buton, Y.Yann Le Cunff and F.François Coste. EnzBert: Deep attention network for enzyme class predictions.JOBIM 2022 - Journées Ouvertes en Biologie, Informatique et MathématiquesRennes, FranceJuly 2022, 1-1
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• 44 inproceedingsO.Olivier Dennler, S.Samuel Blanquart, F.François Coste, C.Catherine Belleannée and N.Nathalie Théret. Functional Motif Prediction in ADAMTS-TSL proteins Based on Module(s) and Phenotype(s) Co-appearance.ALPHY/AIEM 2022 - Rencontres Génomique évolutive, Bioinformatique, Alignement et PhylogénieRennes, FranceMarch 2022
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• 45 thesisP.Pauline Hamon-Giraud. Comparative analysis of genome scale metabolic networks in brown algae.Université Rennes 1June 2022
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• 46 inproceedingsC.Camille Juigné. Integration and analysis of heterogeneous biological data modelled with multilayer graphs for a better understanding of feed efficiency.Séminaire DIGIT-BIO INRAEEcully, FranceDecember 2022
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• 47 inproceedingsH.Hugo Talibart, F.François Coste and M.Mathilde Carpentier. PPalign: optimal alignment of Potts models representing proteins with direct coupling information.ISMB 2022 - 30th Conference on Intelligent Systems for Molecular BiologyMadison, United StatesJuly 2022, 1-1
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• 48 thesisY.Yael Tirlet. Integration of farming and environmental data thanks to Web semantic technologies.Université de Rennes 1June 2022
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