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  40. 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 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  54. 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  66.

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  46 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 19, 32.

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 and Padmet-utils packages), 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  64, extremophile bacteria  49 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)  42. 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  67.

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  43, and multi-layer information in large-scale Petri nets  38. 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 software Cell ASP Optimizer (Caspo) constitutes a pipeline for automated reasoning on logical signaling networks (learning, classifying, designing experimental perturbations, identifying controllers, take time-series into account)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  38. 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:
  2021: (1) release 4.3.1, (2) update documentation, (3) add support for date datatype, (4) improve support for CURIEs, (5) add support for negation (still limited), (6) improve UI for dataset management, (7) add support for semantic expansion to superclasses in queries
• 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:
  (1) Improvements of the pipeline and its continuous integration (2) Release of version 1.5.0 (3) Development of m2m-analysis subpipeline
• 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
• 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:
  Implementation of a new and faster version of paloma in modern C++ relying on a new definition of 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:
  The workflow have been extended to enable modeling position-specific insertion and deletion costs. The rescaling of the models in MakePotts has been rewritten in C++ increasing the speed of the program tenfold and a mean field approach (mfDCA) has been integrated as an option in MakePotts for the inference of Potts models. The exploration and the optimisation of the hyper parameters of the method rely now on the Optuna framework which provides better analysis tools.
• URL:
  https://­www-dyliss.­irisa.­fr/­ppalign/
• Publications:
  hal-02862213, hal-02402646, hal-03264248
• Authors:
  Hugo Talibart, François Coste
• Contact:
  François Coste

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.
• URL:
  https://­gitlab.­inria.­fr/­nbuton/­tfpc
• Contact:
  Nicolas Buton
• Participants:
  Nicolas Buton, Yann Le Cunff, François Coste

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.
• 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.
• URL:
  https://­github.­com/­AuReMe/­aucome
• Contact:
  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.
• Publication:
  hal-02395024
• Contact:
  Anne Siegel
• Participants:
  Arnaud Belcour, Anne Siegel, Clemence Frioux, Meziane Aite

INSILIANCE: co-supervised PhD

Participants: Méziane Aite, Olivier Dameron.

Biofortis Mérieux nutrisciences: internship and data sharing

Participants: Yann Le Cunff, Baptiste Ruiz.

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

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, Hugo Talibart.

The project gathers 18 partners from Station Biologique de Roscoff (coordinator), CNRS, IFREMER, UEB, UBO, UBS, ENSCR, University of Nantes, INRA, AgroCampus, and the industrial field in order to foster biotechnology applications within the seaweed field. Dyliss is co-leader of the WP related to the establishment of a virtual platform for integrating omics studies on seaweed and the integrative analysis of seaweed metabolism. Major objectives are the building of brown algae metabolic maps, metabolic flux analysis and the selection of symbiotic bacteria for brown algae. We will also contribute to the prediction of specific enzymes (sulfatases and haloacid dehalogenase)14. 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.

10.2.1 Programs funded by Inria

PROLIFIC

Participants: Corentin Raphalen, Anne Siegel.

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

Participants: François Coste.

11.1.1 Scientific events: organisation

11.1.2 Journal

11.1.3 Invited talks

• "Ontologies and Semantic Web in Life Sciences" CATI SICPA INRAe (May 20th 2021, O. Dameron)
• "Semantic Web hands-on tutorial" CATI SICPA INRAe (July 07th 2021, O. Dameron)
• "Modeling biological systems for unconventional organisms: from dynamical systems to automated reasoning" GDR Intelligence Artificielle (May 2021, A. Siegel)
• "A quoi peut servir un GT égalité femmes-hommes ?", Seminar of the LIP6 laboratory (June 2021, A. Siegel)
• "Metabolic network models : from formats to workflows", [BC]2 workshop -Toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, Basel (September 2021, A. Siegel)
• "Building genome scale metabolic models: good (or at least not too bad) practices ?", Workshop sur la Modélisation du métabolisme, Bordeaux (November 2021, A. Siegel)
• "Harnessing prior knowledge to improve AI algorithms for patients’ representation and classification", Knowledge Summit 3 : IA & Santé (November 2021, Y. le Cunff)

11.1.4 Scientific expertise

11.1.5 Research administration

11.2.1 Teaching tracks responsibilities

• 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]

11.2.2 Course responsibilities

• "Method", Master 2 in Computer Sciences, Univ. Rennes 1 [E. Becker]
• "Statistiques appliquées", 3rd year in Fundamental Computer Sciences, ENS Rennes [E. Becker]
• "Introduction to computational ecology", Master 2 in Ecology, Univ. Rennes 1 [E. Becker]
• "Object-oriented programming", Master 1 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Advanced R for data analysis", Master 1 in Ecology + Master 1 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Insertion Professionnelle et tables rondes", Master 1 and Master 2 in Bioinformatics, Univ. Rennes 1 [E. Becker]
• "Atelier de Biostatistiques", 2nd year Biology, Univ Rennes 1 [E. Becker]
• "Internship", Master 1 in Computer Sciences, Univ. Rennes 1 [C. Belleannée]
• "Supervised machine learning", Master 2 in Computer Sciences, Univ Rennes 1 [F. Coste]
• "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 & 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]

11.2.3 Teaching

• Licence : E. Becker, "Atelier de Biostatistiques", 34h, 2nd year in Biology, Univ. Rennes 1, France
• Licence : E. Becker, "Statistiques Appliquées", 20h, 3rd year in Fundamental Computer Sciences, ENS Rennes, France
• Master : E. Becker, "Object oriented programming", 56h, Master 1 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Advanced R for data analysis", 36h, Master 1 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Introduction to computational ecology", 34h, Master 2 in Ecology, Univ. Rennes 1, France
• Master : E. Becker, "Method", 15h, Master 2 in Computer Sciences, Univ. Rennes 1, France
• Master : E. Becker, "Insertion Professionnelle et tables rondes", 6h, Master 1 and Master 2 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Systems Biology : biological netorks", 27h, Master 2 in Bioinformatics, Univ. Rennes 1, France
• Master : E. Becker, "Introduction to Bioinformatics", 3h, Master MEEF Biology, Univ. Rennes 1, France.
• Licence: C. Belleannée, Langages formels, 20h, L3 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Projet professionnel et communication, 16h, L1 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Enseignant référent, 20h, L1 informatique, Univ. Rennes 1, France.
• Licence: C. Belleannée, Spécialité informatique : Functional and immutable programming , 42h, L1 informatique, Univ. Rennes 1, France
• Master: C. Belleannée, Algorithmique du texte et bioinformatique, 10h, M1 informatique, Univ. Rennes 1, France
• Master: C. Belleannée, Programmation logique et contraintes, 32h, M1 informatique, Univ. Rennes 1, France
• Master: 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
• Licence: N. Guillaudeux, Projet professionnel et communication, 16h, 1st year Computer Science, Univ. Rennes 1, France
• Licence: N. Guillaudeux, "TPs Python", 36h, 1st year in Biology, Univ. Rennes 1, France
• Licence: M. Louarn, Introduction à la BioInformatique, 6h, L2 Informatique, Univ. Rennes 1, France.
• Licence: M. Louarn, Informatique, 10h, L1 Physique Chimie, Univ. Rennes 1, France.
• Master: M. Louarn, Informatique Médicale Avancée, 2h, M1 Médecine, Univ. Rennes 1, France.
• Master: M. Louarn, Object-oriented programming, 25h, M2 bioinformatique et génomique, Univ. Rennes 1, France.
• Master: M. Louarn, Jury de stage, 6h, M1 bioinformatique et génomique, Univ. Rennes 1, France.
• Master: A. Siegel, Integrative and Systems biology, Master 2 in bioinformatics, Univ. Rennes 1.
• 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

11.2.4 Supervision

• PhD: Hugo Talibart, Comparison of homologous protein sequences using direct coupling information by pairwise Potts model alignments, defended February 24th 2021, supervised by F. Coste and J. Nicolas (GenScale). 32
• PhD: Maël Conan, Approche prédictive pour évaluer la génotoxicité des contaminants de l’environnement, defended 23rd March 2021, supervised by A. Siegel and S. Langouët. 30
• PhD: Nicolas Guillaudeux, Comparer des structures de gènes pour la prédiction de transcrits alternatifs codants chez l’humain, la souris et le chien, defended December 16th 2021, supervised by O. Dameron, S. Blanquart and C. Belleannée. 31
• PhD in progress: Johanne Bakalara, Temporal models of care sequences for the exploration of medico-administrative data, started in Oct. 2018, supervised by T. Guyet (Lacodam), E. Oger (Repères) and O. Dameron.
• PhD in progress: Arnaud Belcour, Inferring Model metabolisms for bacterial ecosystems reduction, started in Oct. 2019, supervised by A. Siegel and S. Blanquart.
• 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: Olivier Dennler, Modular functional characterization of ADAMTL and ADAMTSL protein families, started in Oct. 2019, supervised by N. Theret, F. Coste, S. Blanquart and C. Belleannée.
• 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: Virgilio Kmetzsch Multi-modal analysis of neuroimaging and transcriptomics data in genetically-induced fronto-temporal dementia, started in Oct. 2019, supervised by E. Becker and O. Colliot (INRIA Aramis, ICM Paris)
• PhD in progress: 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).
• 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 2021: Méziane Aite. Identification de nouvelles combinaisons thérapeutiques dans les indications neurologiques, started in Nov. 2020, supervised by O. Dameron and V. Lafon (Insiliance).
• PhD interrupted in 2021: Pierre Vignet, Identification et conception expérimentale de nouveaux agents thérapeutiques à partir d’un modèle informatique des réseaux d’influence du TGF-beta dans les pathologies hépatiques chroniques, started in Dec. 2018, supervised by N. Théret and A. Siegel.
• M2 Internship: Ève Barré, Analyse de réseaux de régulation de la transcription et priorisation des facteurs de transcription, introduction de variants non codants, Jan. – Jul. 2021, co-supervised by M. Louarn and O. Dameron
• M2 Internship: Benjamin Blanc, Detection of genomic and proteic recombinations in phages by partial local alignment, Jan. – Jul. 2021, co-supervised by F. Coste
• M2 Internship: Nancy d'Arminio (Erasmus+ exchange with Salerno Univ., Italy) Extraction of yeast ubiquitin ligases -protein substrates relations from the litterature, Apr. – Jul. 2021, supervised by E. Becker
• M2 Internship: Sarah Guinchard, Characterizing MADE2, an ancient miniature transposable element, in the human genome, Apr. – Sept. 2021, supervised by C. Belleannée
• M2 Internship: Baptiste Ruiz, Intégration de données hétérogènes pour la caractérisation de patientes atteintes du cancer de l'ovaire : Microbiotes, données cliniques et habitudes alimentaires, Mar. – Aug. 2021, supervised by Y. Le Cunff
• M2 Internship: Kerian Thuillier, Inferring boolean rules controling hybrid models inspired by systems biology, Feb. – Jul. 2021, supervised by A. Siegel
• M2 Internship: Leo Maury, "Machine learning applied to characterize cell death in liver", Apr.-Jun. 2021, co-supervised by Y. Le Cunff and J. Le Seyec (IRSET)
• M1 Research Project: Malo Revel, Luca Paparazzo, Learning substitutable languages, Sept. 2020 – Jun. 2021, supervised by F. Coste.

11.2.5 Juries

• Member of PhD thesis juries (9):
  • N. Guillaudeux, Université de Rennes 1 [C. Belleannée, S. Blanquart, O. Dameron]
  • C. Roussel, Ecole Normale Supérieure Paris [F. Coste, president]
  • N. Romashchenko, Université de Montpellier, déc. 2021 [F. Coste]
  • H. Talibart, Université de Rennes 1 [F. Coste]
  • M. Balluet, Université de Rennes 1 [O. Dameron, president]
  • M. Conan, Université de Rennes 1 [A. Siegel]
  • A. Weber, Sorbonne Université [A. Siegel]
  • A. Desoeuvres, Univ. Montpellier [A. Siegel]
  • V. Mataigne, Univ Rennes [A. Siegel].

11.3.1 Articles and contents

• Science en Cour[t]s15 Many of our current and former PhD students (N. Guillaudeux, O. Dennler, M. Louarn, M.Wéry, L. Bourneuf, H. Talibart, A. Antoine-Lorquin, C. Bettembourg, J. Coquet, V. Delannée, G. Garet, S. Prigent) have been heavily involved in organization of a local Popularization Festival where PhD. students explain their thesis via short movies. The movies are presented to a professional jury composed of artists and scientists, and of high-school students. Previous years films can be viewed on the festival website.
• Les décodeuses du numérique. Co-supervision of a comic book gathering the portraits of 12 female computer scientists. The book was sent to all French high schools and is freely available on line (more than 20,000 views in monthes and 5,000 downloads since september 2021). 16 [A. Siegel]

11.3.2 Education

• General introduction to bioinformatics and presentation of the bioinformatics-related careers, Lycée du Léon, Landivisiau December 10 2021 (postponed because of covid-related restrictions) [O. Dameron]
• ESIR (engineer school, Rennes) table-ronde sur la parité et les métiers du numérique au féminin [A. Siegel]
• J'peux pas j'ai informatique - Prof Since 2018, the Commission Égalité Femmes-Hommes has been hosting more than 150 fifth-grade students every year, during a "J'peux pas j'ai informatique" day to raise awareness of the very wide diversity of digital sciences. In 2021, in partnership with the rectorat and the association Femmes & Sciences, IRISA and Inria Rennes Bretagne Atlantique have also offered this training to teachers, so that each and every one of them can make the training and the workshops their own and duplicate them within their own school. [A. Siegel]
• Rencontre des jeunes mathématiciennes et informaticiennes, Rennes. [A. Siegel]
• LCodent L Creent the program introduces high school girls to programming during workshops led by computer science female phD students. The approach, based on creativity, allows the college girls to appropriate the concepts necessary for the realization of computer programs in 8 sessions carried out in high schools. [C. Juigné]

11.3.3 Interventions

• Présentation CNU section 27, Journée doctorants D3, juin 2021 [F. Coste]
• Journée des 20 ans de la Mission pour la place des femmes du CNRS. le Rôle des référentes et référents parité d'un laboratoire [A. Siegel]
• Radio France International, Emission "autour de la question". Quelles perspectives les nouveaux métiers du numérique offrent-ils aux femmes?17 [A. Siegel]

International journals

• 11 articleA.Aymeric Antoine-Lorquin, P.Peter Arensburger, A.Ahmed Arnaoty, S.Sassan Asgari, M.Martine Batailler, L.Linda Beauclair, C.Catherine Belleannée, N.Nicolas Buisine, V.Vincent Coustham, S.Serge Guyetant, L.Laura Helou, T.Thierry Lecomte, B.Bruno Pitard, I.Isabelle Stévant and Y.Yves Bigot. Two repeated motifs enriched within some enhancers and origins of replication are bound by SETMAR isoforms in human colon cells.Genomics11332021, 1589-1604
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• 12 articleS.Samuel Blanquart, M.Mathieu Groussin, A.Aline Le Roy, G. J.Gergely J Szöllosi, E.Eric Girard, B.Bruno Franzetti, M.Manolo Gouy and D.Dominique Madern. Resurrection of Ancestral Malate Dehydrogenases Reveals the Evolutionary History of Halobacterial Proteins : Deciphering Gene Trajectories and Changes in Biochemical Properties.Molecular Biology and Evolution2021, 1-44
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• 13 articleM.Mael Conan, N.Nathalie Theret, S.Sophie Langouet and A.Anne Siegel. Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.BMC Bioinformatics221September 2021, 450
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• 14 articleJ.Jean Girard, G.Goulven Lanneau, L.Ludovic Delage, C.Cédric Leroux, A.Arnaud Belcour, J.Jeanne Got, J.Jonas Collén, C.Catherine Boyen, A.Anne Siegel, S. M.Simon M Dittami, C.Catherine Leblanc and G. .Gabriel V Markov. Semi-Quantitative Targeted Gas Chromatography-Mass Spectrometry Profiling Supports a Late Side-Chain Reductase Cycloartenol-to-Cholesterol Biosynthesis Pathway in Brown Algae.Frontiers in Plant Science122021, 1-10
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• 15 articleV.Vincent Henry, I.Ivan Moszer, O.Olivier Dameron, L.Laura Vila Xicota, B.Bruno Dubois, M.-C.Marie-Claude Potier, M.Martin Hofmann-Apitius and O.Olivier Colliot. Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer’s disease.Database - The journal of Biological Databases and CurationFebruary 2021, 1-33
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• 16 articleE.Elham Karimi, E.Enora Geslain, A.Arnaud Belcour, C.Clémence Frioux, M.Méziane Aite, A.Anne Siegel, E.Erwan Corre and S. M.Simon M Dittami. Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.PeerJ9May 2021, 1-24
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• 17 articleM.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.Bioinformatics2022
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• 18 articleF.François Moreews, H.Hugo Simon, A.Anne Siegel, F.Florence Gondret and E.Emmanuelle Becker. PAX2GRAPHML: a Python library for large-scale regulation network analysis using BIOPAX.Bioinformatics37242021, 4889-4891
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• 19 articleH.Hugo Talibart and F.François Coste. PPalign: optimal alignment of Potts models representing proteins with direct coupling information.BMC Bioinformatics221December 2021, 1-22
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• 20 articleQ.Qikun Xing, G.Guiqi Bi, M.Min Cao, A.Arnaud Belcour, M.Méziane Aite and Y.Yunxiang Mao. Comparative Transcriptome Analysis Provides Insights into Response of Ulva compressa to Fluctuating Salinity Conditions.Journal of Phycology574August 2021, 1295-1308
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International peer-reviewed conferences

• 21 inproceedingsM.Matthieu Bouguéon, P.Pierre Boutillier, J.Jerome Feret, O.Octave Hazard and N.Nathalie Theret. A Kappa model for hepatic stellate cells activation by TGFB1.CMSB 2021 - 19th International Conference on Computational Methods in Systems BiologyBordeaux / Virtual, FranceSeptember 2021
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• 22 inproceedingsK.Kerian Thuillier, C.Caroline Baroukh, A.Alexander Bockmayr, L.Ludovic Cottret, L.Loïc Paulevé and A.Anne Siegel. Learning Boolean controls in regulated metabolic networks: a case-study.CMSB 2021 - 19th International Conference on Computational Methods in Systems BiologyBordeaux, FranceSpringer2021
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Conferences without proceedings

• 23 inproceedingsJ.Johanne Bakalara, T.Thomas Guyet, O.Olivier Dameron, A.André Happe and E.Emmanuel Oger. An extension of chronicles temporal model with taxonomies -Application to epidemiological studies.HEALTHINF 2021 - 14th International Conference on Health Informaticsonline, FranceFebruary 2021, 1-10
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• 24 inproceedingsC.Clémence Frioux, A.Arnaud Belcour, M.Meziane Aite, A.Anthony Bretaudeau, F.Falk Hildebrand and A.Anne Siegel. Metabolic complementarity applied to the screening of microbiota and the identification of key species.JOBIM 2021 - Journées Ouvertes en Biologie, Informatique et MathématiquesParis, FranceJuly 2021
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• 25 inproceedingsC.Clémence Frioux, A.Arnaud Belcour, M.Méziane Aite, A.Anthony Bretaudeau, F.Falk Hildebrand and A.Anne Siegel. Metabolic complementarity applied to the screening of microbiota and the identification of key species.MPA 2021 - 8th Metabolic Pathway AnalysisKnoxville, TN, United StatesAugust 2021, 1-27
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• 26 inproceedingsC.Clémence Frioux, A.Arnaud Belcour, M.Méziane Aite, A.Anthony Bretaudeau, F.Falk Hildebrand and A.Anne Siegel. Metabolic complementarity applied to the screening of microbiota and the identification of key species.CMSB 2021 - 19th International Conference on Computational Methods in Systems BiologyBordeaux, FranceSeptember 2021, 1-27
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• 27 inproceedingsT.Thomas Guyet, T.Tristan Allard, J.Johanne Bakalara and O.Olivier Dameron. An open generator of synthetic administrative healthcare databases.Actes de l'atelier Intelligence Artificielle et Santé (IAS)IAS 2021 - Atelier Intelligence Artificielle et SantéBordeaux (virtuel), FranceJune 2021, 1-8
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• 28 inproceedingsF.FAKIH Ibrahim, J.Jeanne Got, A.Anne Siegel, E.Evelyne Forano and R.Rafael Munoz Tamayo. Genome-scale network reconstruction of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85.12. International Symposium on Gut MicrobiologyEn ligne, France2021
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Scientific book chapters

• 29 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 Tools2021, 1-76
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Doctoral dissertations and habilitation theses

• 30 thesisM.Maël Conan. Predictive approach to asses the genotoxicity of environmental contaminants.Université Rennes 1March 2021
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• 31 thesisN.Nicolas Guillaudeux. To compare gene structures for prediction of alternative coding transcripts in human,mouse and dog.Université Rennes 1December 2021
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• 32 thesisH.Hugo Talibart. Comparison of homologous protein sequences using direct coupling information by pairwise Potts model alignments.Université Rennes 1February 2021
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Other scientific publications

• 33 thesisB.Benjamin Blanc. Détection de recombinaisons génomiques et protéomiques homologues par alignement multiple local et partiel.Rennes 1June 2021
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• 34 inproceedingsM.Matthieu Bouguéon, P.Pierre Boutillier, J.Jerome Feret, O.Octave Hazard and N.Nathalie Theret. A Kappa model for hepatic stellate cells activation by TGFB1.CompSysBio 2021 - Advanced Lecture Course on Computational Systems BiologyAussois, FranceNovember 2021
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• 35 inproceedingsO.Olivier Dennler, S.Samuel Blanquart, F.François Coste, C.Catherine Belleannée and N.Nathalie Theret. Phylogenetic Functional Module Characterization of the ADAMTS / ADAMTS like Protein Family.JOBIM : Journées Ouvertes en Biologie, Informatique & MathématiquesParis, FranceJuly 2021
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• 36 inproceedingsO.Olivier Dennler, S.Samuel Blanquart, F.François Coste, C.Catherine Belleannée and N.Nathalie Theret. Phylogenetic Functional Module Characterization of the ADAMTS / ADAMTS like Protein Family.WABI 2021 - Workshop on Algorithms in BioinformaticsChicago (Online), United StatesAugust 2021
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• 37 inproceedingsC.Clémence Frioux, A.Arnaud Belcour, M.Méziane Aite, A.Anthony Bretaudeau, F.Falk Hildebrand and A.Anne Siegel. Assessment of metabolic complementarity in large-scale microbiotas for the identification of key species.IHMC 2021 - 8th International Human Microbiome Consortium CongressBarcelone, SpainJune 2021, 1
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