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5. Bacterial strain nomenclature in the genomic era: Life Identification Numbers using a gene-by-gene approach

Author

Palma, Federica, primary, Hennart, Melanie, additional, Jolley, Keith A., additional, Crestani, Chiara, additional, Wyres, Kelly L., additional, Bridel, Sebastien, additional, Yeats, Corin A., additional, Brancotte, Bryan, additional, Raffestin, Brice, additional, David, Sophia, additional, Lam, Margaret M. C., additional, Izdebski, Radosław, additional, Passet, Virginie, additional, Rodrigues, Carla, additional, Rethoret-Pasty, Martin, additional, Maiden, Martin C. J., additional, Aanensen, David M., additional, Holt, Kathryn E., additional, Criscuolo, Alexis, additional, and Brisse, Sylvain, additional

Published
2024
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6. BioConvert: a comprehensive format converter for life sciences

Author

Caro, Hugo, primary, Dollin, Sulyvan, additional, Biton, Anne, additional, Brancotte, Bryan, additional, Desvillechabrol, Dimitri, additional, Dufresne, Yoann, additional, Li, Blaise, additional, Kornobis, Etienne, additional, Lemoine, Frédéric, additional, Maillet, Nicolas, additional, Perrin, Amandine, additional, Traut, Nicolas, additional, Néron, Bertrand, additional, and Cokelaer, Thomas, additional

Published
2023
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7. ConQuR-Bio: Consensus Ranking with Query Reformulation for Biological Data

Author

Brancotte, Bryan, Rance, Bastien, Denise, Alain, Cohen-Boulakia, Sarah, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Galhardas, Helena, editor, and Rahm, Erhard, editor

Published
2014
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8. The IFB Catalogue

Author

Brancotte, Bryan, Kengni, Hippolyte, Rosnet, Thomas, Bouri, Laurent, Ison, Jon, Sand, Olivier, Chiapello, Hélène, Gaignard, Alban, Milanesi, Sylvain, van Helden, Jacques, and Ménager, Hervé

Abstract

The IFB Catalogue is a centralized database developed as part of the IFB Distributed national environment of services in Bioinformatics. Its aim is to ensure the visibility and accessibility of the Bioinformatics resources provided and maintained by the french community, whether these are research labs or service platforms, in a structured and open database that guarantees their FAIRness. Such resources can be software tools, databases, computing resources, individual expertises, platforms, trainings and training materials. This catalogue stores the metadata describing their properties (e.g. the licence of a software tool) and linking them (e.g. the publication of a database by a given team). The primary role of the Catalogue is to help users of Bioinformatics services. Through the integration of the data in the IFB website (https://www.france-bioinformatique.fr), it provides an overview of the various resources provided by the community, and supports the needs of end-users (who can perform a given kind of analysis? where is a tool available?). It is also synchronized with related international catalogues (e.g. ELIXIR bio.tools[1] , TeSS[2]), and promotes the reusability of the data through the publication of Bioschemas[3] markup and REST APIs as well as the use of the EDAM ontology[4]. The backend server is available at https://catalogue.france-bioinformatique.fr/. The code of the Catalogue database backend is openly available on https://github.com/IFB-ElixirFr/ifbcat

Published
2022
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9. Genomic library of Bordetella

Author

Bridel, Sébastien, primary, Bouchez, Valérie, additional, Brancotte, Bryan, additional, Hauck, Sofia, additional, Armatys, Nathalie, additional, Landier, Annie, additional, Mühle, Estelle, additional, Guillot, Sophie, additional, Toubiana, Julie, additional, Maiden, Martin C.J., additional, Jolley, Keith A., additional, and Brisse, Sylvain, additional

Published
2022
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10. EDAM Browser: visualization of EDAM and annotated bioinformatics resources

Author

Eldakroury, Hager, Dhamija, Sakshi, Rathi, Rashika, Patoliya, Drashti, Singh, Guneet, Yadav, Pooja, d'Oleo, Kelly, Cherop, Marlene, Che Nico, Tawah Peggy, Kalaš, Matúš, Ménager, Hervé, Brancotte, Bryan, Ain Shams University, Atal Bihari Vajpayee-Indian Institute of Information Technology and Management [Gwalior] (ABV-IIITM), Indian Institute of Technology, University of Bergen (UiB), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Outreachy internship funding program, and ISMB/ECCB 2021

Subjects
Life science, Open Science, Ontology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Data management, FAIR, Data visualisation
Abstract

International audience; EDAM is an ontology widely used to describe bioinformatics resources (tools, databases, workflows, training, etc.). However, the exploration of the ontology itself by users who might not be ontology experts remains sub-optimal, with most of the usual interfaces being either too complex, too slow, or too generic.Here we present EDAM Browser, which lets users explore EDAM with an interface tailored to its structure and properties. The features of this ontology browser are focused on the needs of EDAM's end-users, who search and explore the ontology to find or annotate bioinformatics resources.A unique feature of EDAM Browser is the visualization of all ""positions"" of a selected concept in the full ontology ""tree"" (DAG), with all paths to the root highlighted. One of EDAM Browser's main added values is the aggregated search across various registries (e.g. bio.tools, TeSS, BioSphere), and its speed.The lightweight architecture makes it easy to download and run EDAM Browser on any server or personal computer, either as a local HTML file or on a web server. EDAM Browser has also been used for other ontologies, and making the reuse smoother and more usable is part of the ongoing work. Contributions are welcome!

Published
2021

11. The iPPI-DB initiative: a community-centered database of protein–protein interaction modulators

Author

Torchet, Rachel, Druart, Karen, Ruano, Luis Checa, Moine-Franel, Alexandra, Borges, Hélène, Doppelt-Azeroual, Olivia, Brancotte, Bryan, Mareuil, Fabien, Nilges, Michael, Ménager, Hervé, Sperandio, Olivier, Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Département de Biologie structurale et Chimie - Department of Structural Biology and Chemistry, Institut Pasteur [Paris] (IP), none declared, This work used the computational and storage services (TARS cluster, VMHosting) provided by the IT department at Institut Pasteur, Paris. The authorswish to acknowledge in particular the help and technical advice of Eric Deveaud,Emmanuel Guichard, Thomas Me ́nard and Youssef Ghorbal (IT Department,Institut Pasteur). They also want to acknowledge the technical help of Tru Huynh(Structural Bioinformatics Unit, Institut Pasteur). They thank Jon Ison, BenjaminBardiaux and Pascal Campagne for their proofreading of the paper.Marvin JS (20.5.0, 2020, http://www.chemaxon.com) is used for drawingand displaying chemical structures in both Query mode and Contributionmode of iPPI-DB. Pipeline Pilot (server 19.1) is used to prepare the DrugBankdatabase from a SDF file prior to chemical similarity search 202 (2020), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Spectrométrie de Masse pour la Biologie – Mass Spectrometry for Biology (UTechS MSBio), Centre National de la Recherche Scientifique (CNRS)-Centre de Ressources et de Recherche Technologique - Center for Technological Resources and Research (C2RT), Institut Pasteur [Paris]-Institut Pasteur [Paris], Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Etude de la dynamique des protéomes (EDyP), BioSanté (UMR BioSanté), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Biologie Computationnelle (ex C3BI), Bioinformatique structurale - Structural Bioinformatics, and Druart, Karen

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], AcademicSubjects/SCI01060, Databases and Ontologies, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Original Papers, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; Abstract Motivation One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. Results Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well. Availability and implementation The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021
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12. Viral Host Range database, an online tool for recording, analyzing and disseminating virus-host interactions

Author

Lamy-Besnier, Quentin, Brancotte, Bryan, Ménager, Hervé, Debarbieux, Laurent, Bactériophage, bactérie, hôte - Bacteriophage, bacterium, host, Institut Pasteur [Paris] (IP), Université Paris Cité (UPCité), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), This work has been supported by grant ANR-19-AMRB-0002, ANR-19-AMRB-0002,MAPVAP,Évaluation préclinique et mécanistique de deux cocktails de bactériophages ciblant Pseudomonas aeruginosa et Escherichia coli multirésistants pour le traitement des pneumonies acquises sous ventilation(2019), Institut Pasteur [Paris], Université de Paris (UP), and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)

Subjects
AcademicSubjects/SCI01060, viruses, [SDV]Life Sciences [q-bio], Databases and Ontologies, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], complex mixtures, Applications Notes
Abstract

Abstrtact Motivation Viruses are ubiquitous in the living world, and their ability to infect more than one host defines their host range. However, information about which virus infects which host, and about which host is infected by which virus, is not readily available. Results We developed a web-based tool called the Viral Host Range database to record, analyze and disseminate experimental host range data for viruses infecting archaea, bacteria and eukaryotes. Availability and implementation The ViralHostRangeDB application is available from https://viralhostrangedb.pasteur.cloud. Its source code is freely available from the Gitlab instance of Institut Pasteur (https://gitlab.pasteur.fr/hub/viralhostrangedb).

Published
2020
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13. High-Resolution Typing of Staphylococcus epidermidis Based on Core Genome Multilocus Sequence Typing To Investigate the Hospital Spread of Multidrug-Resistant Clones

Author

Jamet, Anne, primary, Guglielmini, Julien, additional, Brancotte, Bryan, additional, Coureuil, Mathieu, additional, Euphrasie, Daniel, additional, Meyer, Julie, additional, Roux, Johanna, additional, Barnier, Jean-Philippe, additional, Bille, Emmanuelle, additional, Ferroni, Agnès, additional, Magny, Jean-François, additional, Bôle-Feysot, Christine, additional, Charbit, Alain, additional, Nassif, Xavier, additional, and Brisse, Sylvain, additional

Published
2021
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16. Community curation of bioinformatics software and data resources

Author

Ison, Jon, Ménager, Hervé, Brancotte, Bryan, Jaaniso, Erik, Salumets, Ahto, Raček, Tomáš, Lamprecht, Anna-Lena, Palmblad, Magnus, Kalaš, Matúš, Chmura, Piotr, Hancock, John M, Schwämmle, Veit, Ienasescu, Hans-Ioan, Sub Software Technology, Software Technology, Sub Software Technology, Software Technology, National Life Science Supercomputing Center [Kongens Lyngby, Denmark] (Computerome), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institute of Computer Science [University of Tartu, Estonie], University of Tartu, Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Faculty of Informatics [Brno] (FI / MUNI), Masaryk University [Brno] (MUNI), Department of Information and Computing Sciences [Utrecht], Utrecht University [Utrecht], Center for Proteomics and Metabolomics [Leiden] (CPM), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Informatics [Bergen] (UiB), University of Bergen (UiB), Novo Nordisk Foundation Center for Protein Research (CPR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), ELIXIR Hub [Cambridge], University of Southern Denmark (SDU), We acknowledge with gratitude the support of our funders: The Danish Ministry of Higher Education and Science and ELIXIR-EXCELERATE under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 676559), European Project: 676559,H2020,H2020-INFRADEV-1-2015-1,ELIXIR-EXCELERATE(2015), Technical University of Denmark [Lyngby] (DTU), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), and University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)

Subjects
AcademicSubjects/SCI01060, Computer science, community driven, registry, 03 medical and health sciences, Software, Bioinformatics software, Humans, Molecular Biology, database, 030304 developmental biology, Life Scientists, 0303 health sciences, business.industry, software, 030302 biochemistry & molecular biology, Community Participation, Computational Biology, bioinformatics, curation, Service provider, Data science, Data resources, Europe, Database Management Systems, Problem Solving Protocol, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Information Systems
Abstract

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

Published
2019
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17. Community curation of bioinformatics software and data resources

Author

Ison, Jon, Ménager, Hervé, Brancotte, Bryan, Jaaniso, Erik, Salumets, Ahto, Raček, Tomáš, Lamprecht, Anna-Lena, Palmblad, Magnus, Kalaš, Matúš, Chmura, Piotr Jaroslaw, Hancock, John M, Schwämmle, Veit, Ioan Ienasescu, Hans, Ison, Jon, Ménager, Hervé, Brancotte, Bryan, Jaaniso, Erik, Salumets, Ahto, Raček, Tomáš, Lamprecht, Anna-Lena, Palmblad, Magnus, Kalaš, Matúš, Chmura, Piotr Jaroslaw, Hancock, John M, Schwämmle, Veit, and Ioan Ienasescu, Hans

Abstract

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

Published
2020

18. Insyght : Analyse evolutionary conserved CDS, orthologs, syntenies, pan-genome, fusion, etc., for your bacteria of interest​

Author

Lacroix, Thomas, Loux, Valentin, Lorenzo, Jonathan, Brancotte, Bryan, Blanchet, Christophe, Gibrat, Jean-Francois, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), and Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
cloud ressources, conserved synteny, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], multiple bacterial genomes, comparative genomics, orthology, [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS, visualization
Abstract

National audience

Published
2018

20. Community curation of bioinformatics software and data resources

Author

Sub Software Technology, Software Technology, Ison, Jon, Ménager, Hervé, Brancotte, Bryan, Jaaniso, Erik, Salumets, Ahto, Raček, Tomáš, Lamprecht, Anna-Lena, Palmblad, Magnus, Kalaš, Matúš, Chmura, Piotr, Hancock, John M, Schwämmle, Veit, Ienasescu, Hans-Ioan, Sub Software Technology, Software Technology, Ison, Jon, Ménager, Hervé, Brancotte, Bryan, Jaaniso, Erik, Salumets, Ahto, Raček, Tomáš, Lamprecht, Anna-Lena, Palmblad, Magnus, Kalaš, Matúš, Chmura, Piotr, Hancock, John M, Schwämmle, Veit, and Ienasescu, Hans-Ioan

Published
2019

22. Multi-Cloud deployment for microbial genomes analysis

Author

Lorenzo, Jonathan, Brancotte, Bryan, Lacroix, Thomas, Bedri, Mohamed, Gibrat, Jean-Francois, Blanchet, Christophe, Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), UMS 3601 Institut Français de Bioinformatique IFB-core, Centre National de la Recherche Scientifique (CNRS), and UMS 3601 Institut Français de Bioinformatique, IFB-core

Subjects
bacterial genomics, [SDV]Life Sciences [q-bio], synteny, bioinformatics tools, virtual environment, cloud infrastructures
Abstract

Short paper = poster acte online Short paper = poster acte onlineShort paper = posteracte online; Multi-Cloud deployment for microbial genomes analysis. JOBIM 2017 - Journées Ouvertes Biologie Informatique Mathématiques

Published
2017

24. Rank aggregation with ties : algorithms, user guidance et applications to biologicals data

Author

Brancotte, Bryan, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université Paris Sud - Paris XI, Sarah Cohen-Boulakia, and Alain Denise

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Exact solution, Topk, Interrogation de sources biomédicale, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Optimal Kemey ranking, Top-k, Classement de Kemeny optimal, Agrégation de classements, NP-difficile, Benchmark, Complexity results, Solution exact, Agrégation de préférences, Preference aggregation, Résultat de complexité, NP-Hard, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Rank aggregation, Querying biomedical sources, Guidance, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

The rank aggregation problem is to build consensus among a set of rankings (ordered elements). Although this problem has numerous applications (consensus among user votes, consensus between results ordered differently by different search engines ...), computing an optimal consensus is rarely feasible in cases of real applications (problem NP-Hard). Many approximation algorithms and heuristics were therefore designed. However, their performance (time and quality of product loss) are quite different and depend on the datasets to be aggregated. Several studies have compared these algorithms but they have generally not considered the case (yet common in real datasets) that elements can be tied in rankings (elements at the same rank). Choosing a consensus algorithm for a given dataset is therefore a particularly important issue to be studied (many applications) and it is an open problem in the sense that none of the existing studies address it. More formally, a consensus ranking is a ranking that minimizes the sum of the distances between this consensus and the input rankings. Like much of the state-of-art, we have considered in our studies the generalized Kendall-Tau distance, and variants. Specifically, this thesis has three contributions. First, we propose new complexity results associated with cases encountered in the actual data that rankings may be incomplete and where multiple items can be classified equally (ties). We isolate the different "features" that can explain variations in the results produced by the aggregation algorithms (for example, using the generalized distance of Kendall-Tau or variants, pre-processing the datasets with unification or projection). We propose a guide to characterize the context and the need of a user to guide him into the choice of both a pre-treatment of its datasets but also the distance to choose to calculate the consensus. We finally adapt existing algorithms to this new context. Second, we evaluate these algorithms on a large and varied set of datasets both real and synthetic reproducing actual features such as similarity between rankings, the presence of ties and different pre-treatments. This large evaluation comes with the proposal of a new method to generate synthetic data with similarities based on a Markov chain modeling. This evaluation led to the isolation of datasets features that impact the performance of the aggregation algorithms, and to design a guide to characterize the needs of a user and advise him in the choice of the algorithm to be use. A web platform to replicate and extend these analyzes is available (rank-aggregation-with-ties.lri.fr). Finally, we demonstrate the value of using the rankings aggregation approach in two use cases. We provide a tool to reformulating the text user queries through biomedical terminologies, to then query biological databases, and ultimately produce a consensus of results obtained for each reformulation (conqur-bio.lri.fr). We compare the results to the references platform and show a clear improvement in quality results. We also calculate consensus between list of workflows established by experts in the context of similarity between scientific workflows. We note that the computed consensus agree with the expert in a very large majority of cases.; L'agrégation de classements consiste à établir un consensus entre un ensemble de classements (éléments ordonnés). Bien que ce problème ait de très nombreuses applications (consensus entre les votes d'utilisateurs, consensus entre des résultats ordonnés différemment par divers moteurs de recherche...), calculer un consensus exact est rarement faisable dans les cas d'applications réels (problème NP-difficile). De nombreux algorithmes d'approximation et heuristiques ont donc été conçus. Néanmoins, leurs performances (en temps et en qualité de résultat produit) sont très différentes et dépendent des jeux de données à agréger. Plusieurs études ont cherché à comparer ces algorithmes mais celles-ci n’ont généralement pas considéré le cas (pourtant courant dans les jeux de données réels) des égalités entre éléments dans les classements (éléments classés au même rang). Choisir un algorithme de consensus adéquat vis-à-vis d'un jeu de données est donc un problème particulièrement important à étudier (grand nombre d’applications) et c’est un problème ouvert au sens où aucune des études existantes ne permet d’y répondre. Plus formellement, un consensus de classements est un classement qui minimise le somme des distances entre ce consensus et chacun des classements en entrés. Nous avons considérés (comme une grande partie de l’état-de-art) la distance de Kendall-Tau généralisée, ainsi que des variantes, dans nos études. Plus précisément, cette thèse comporte trois contributions. Premièrement, nous proposons de nouveaux résultats de complexité associés aux cas que l'on rencontre dans les données réelles où les classements peuvent être incomplets et où plusieurs éléments peuvent être classés à égalité. Nous isolons les différents « paramètres » qui peuvent expliquer les variations au niveau des résultats produits par les algorithmes d’agrégation (par exemple, utilisation de la distance de Kendall-Tau généralisée ou de variantes, d’un pré-traitement des jeux de données par unification ou projection). Nous proposons un guide pour caractériser le contexte et le besoin d’un utilisateur afin de le guider dans le choix à la fois d’un pré-traitement de ses données mais aussi de la distance à choisir pour calculer le consensus. Nous proposons finalement une adaptation des algorithmes existants à ce nouveau contexte. Deuxièmement, nous évaluons ces algorithmes sur un ensemble important et varié de jeux de données à la fois réels et synthétiques reproduisant des caractéristiques réelles telles que similarité entre classements, la présence d'égalités, et différents pré-traitements. Cette large évaluation passe par la proposition d’une nouvelle méthode pour générer des données synthétiques avec similarités basée sur une modélisation en chaîne Markovienne. Cette évaluation a permis d'isoler les caractéristiques des jeux de données ayant un impact sur les performances des algorithmes d'agrégation et de concevoir un guide pour caractériser le besoin d'un utilisateur et le conseiller dans le choix de l'algorithme à privilégier. Une plateforme web permettant de reproduire et étendre ces analyses effectuée est disponible (rank-aggregation-with-ties.lri.fr). Enfin, nous démontrons l'intérêt d'utiliser l'approche d'agrégation de classements dans deux cas d'utilisation. Nous proposons un outil reformulant à-la-volé des requêtes textuelles d'utilisateur grâce à des terminologies biomédicales, pour ensuite interroger de bases de données biologiques, et finalement produire un consensus des résultats obtenus pour chaque reformulation (conqur-bio.lri.fr). Nous comparons l'outil à la plateforme de références et montrons une amélioration nette des résultats en qualité. Nous calculons aussi des consensus entre liste de workflows établie par des experts dans le contexte de la similarité entre workflows scientifiques. Nous observons que les consensus calculés sont très en accord avec les utilisateurs dans une large proportion de cas.

Published
2015

25. Interrogation de bases de données biologiques publiques par reformulation de requêtes et classement des résultats avec ConQuR-Bio

Author

Brancotte, Bryan, Rance, Bastien, Denise, Alain, Cohen-Boulakia, Sarah, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche des Cordeliers (CRC), Université Paris Diderot - Paris 7 (UPD7)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut de Biologie Computationnelle (IBC), Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-École pratique des hautes études (EPHE)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Recherche en Informatique ( LRI ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -CentraleSupélec-Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche des Cordeliers ( CRC ), Université Paris Diderot - Paris 7 ( UPD7 ) -École pratique des hautes études ( EPHE ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Scientific Data Management ( ZENITH ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Modeling plant morphogenesis at different scales, from genes to phenotype ( VIRTUAL PLANTS ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de la Recherche Agronomique ( INRA ) -Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR51, Institut de Biologie Computationnelle ( IBC ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut National de la Recherche Agronomique ( INRA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]
Abstract

National audience; L’analyse d'expériences bioinformatiques comprend la comparaison des nouveaux résultats obtenus auxdonnées existantes. Durant ces trente dernières années, les scientifiques ont du faire face à une avalanche dedonnées, de différents types, et présentes dans une multitude de bases de données publiques. L’accès auxdonnées publiques se fait par l’interrogation de portails (tels que le portail Entrez du NCBI) au moyen demots clés. Cependant, deux requêtes très similaires peuvent fournir des ensembles de réponses différentsconduisant l’utilisateur à devoir tester différentes reformulations de ses requêtes (termes synonymes,variantes orthographiques, abréviations).... Les résultats obtenus doivent ensuite être filtrés, comparés... Enoutre, chaque ensemble de résultats est classé par le portail (en utilisant le nombre d'occurrences du mot-clédans chaque résultat). Cependant, lorsque plusieurs reformulations sont considérées, il n'est pas simple deproduire un classement triant par ordre de pertinence l'ensemble des résultats recueillis séparément, d'autantque ces résultats peuvent être fournis par centaines.Dans cette démonstration, nous présentons ConQuR-Bio (http://conqur-bio.lri.fr) qui permet auxutilisateurs d'interroger les bases de données publiques du NCBI tout en générant automatiquement toutes lesreformulations possibles et fournit des réponses triées en utilisant des techniques de consensus de classement(ou agrégation de classements). Notre démonstration montrera l’intérêt de notre approche pour des requêtesbiomédicales, lors de la recherche de gènes issus d’EntrezGene et impliqués dans des maladies.

Published
2015

26. Rank aggregation with ties

Author

Brancotte, Bryan, primary, Yang, Bo, additional, Blin, Guillaume, additional, Cohen-Boulakia, Sarah, additional, Denise, Alain, additional, and Hamel, Sylvie, additional

Published
2015
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28. High-Resolution Typing of Staphylococcus epidermidisBased on Core Genome Multilocus Sequence Typing To Investigate the Hospital Spread of Multidrug-Resistant Clones

Author

Jamet, Anne, Guglielmini, Julien, Brancotte, Bryan, Coureuil, Mathieu, Euphrasie, Daniel, Meyer, Julie, Roux, Johanna, Barnier, Jean-Philippe, Bille, Emmanuelle, Ferroni, Agnès, Magny, Jean-François, Bôle-Feysot, Christine, Charbit, Alain, Nassif, Xavier, and Brisse, Sylvain

Abstract

Staphylococcus epidermidisis a pathogen emerging worldwide as a leading cause of health care-associated infections. A standardized high-resolution typing method to document transmission and dissemination of multidrug-resistant S. epidermidisstrains is needed.

Published
2020
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29. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability.

Author

Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, and Julienne H

Abstract

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10 -60 ). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing., Competing Interests: Declaration of interests M.H.C. has received grant support from Bayer, unrelated to the current work.

Published
2023
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30. The iPPI-DB initiative: a community-centered database of protein-protein interaction modulators.

Author

Torchet R, Druart K, Ruano LC, Moine-Franel A, Borges H, Doppelt-Azeroual O, Brancotte B, Mareuil F, Nilges M, Ménager H, and Sperandio O

Abstract

Motivation: One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets., Results: Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well., Availability and Implementation: The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2021
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31. Community curation of bioinformatics software and data resources.

Author

Ison J, Ménager H, Brancotte B, Jaaniso E, Salumets A, Raček T, Lamprecht AL, Palmblad M, Kalaš M, Chmura P, Hancock JM, Schwämmle V, and Ienasescu HI

Subjects
Computational Biology standards, Database Management Systems, Europe, Humans, Community Participation, Computational Biology methods, Software
Abstract

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools., (© The Author(s) 2019. Published by Oxford University Press.)

Published
2020
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