Handles creating, reading and updating teams.

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            "logo_url": null,
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            "address": "114 rue Edouard Vaillant\r\nUMS INSERM 23/CNRS 3655 AMMICA\r\n94800 Villejuif\r\nFrance",
            "city": "Villejuif",
            "country": "France",
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            "name": "PB-IBENS",
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            "description": "The Plateforme Bioinformatique (PB-IBENS) is a facility of the Institut de Biologie de l’ENS (IBENS). It defines, develops and deploys the hardware and software resources that meet the specific bioinformatics needs of researchers. It is responsible for the maintenance and deployment of the computing cluster “BioClust” accessible to all partners of the LABEX Memolife (IBENS, ESPCI, Collège de France). PB-IBENS also maintains and supports online tools (web and database servers) developed by the IBENS teams, some of which are labeled by the European Infrastructure Elixir. It is involved in bioinformatics training at the ENS, organises seminars and participates to external courses.",
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                "http://edamontology.org/topic_3489"
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            "country": "FRANCE",
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                "",
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                "Programming Languages & Computer Sciences",
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                "Comparative genomics"
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            "expertise_description": "The analysis of the many variations identified during the sequencing process of genes. In order to identify causative mutations, especially if they are missense mutations or null substitutions that only impact mRNA",
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            "address": "Faculté de Médecine de la Timone, \r\n27 Bd Jean Moulin",
            "city": "Marseille",
            "country": "France",
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            "description": "The advances in high-throughput technologies are providing unprecedented opportunities to better understand human diseases. Recent years have in this context witnessed the accumulation of omics approaches and datasets. Biomedicine is further transitioning from multiomics to multimodal datasets: data are not only available at the molecular omics level, as we now have access to signals and images, but also to various datasets related to disease phenotypes, health databases, or drug chemical similarities. The bottleneck now lies in the analysis and integration of these complex, large-scale and heterogeneous datasets. The Systems Biomedicine team bridges the gaps by harnessing digital expertise and developing novel computational approaches.",
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            "expertise_description": "The Systems Biomedicine team is hosting the research group of Paul Villoutreix, laureate of an INSERM Chaire de Professeur Junior.\r\nThe Systems Biomedicine team works in close collaboration with the MABIOS team from the Marseille Mathematics Institute.",
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            "address": "Faculté de Médecine de la Timone\r\n27 Bd Jean Moulin",
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            "country": "France",
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                    "name": "Aix Marseille Univ",
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            "name": "INCa-SLC",
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            "description": "Cette structure a été créée à l'initiative de l'INCa dans le cadre de sa participation à l'\"International  Cancer Genome Consortium\" (ICGC).\r\n \r\nElle a trois missions :\r\n1 collecter ou préparer puis valider les acides nucléiques (ADN normal, ADN tumoral, ARN tumoral,...)  qui   seront   examinés   avec   les   techniques   de  la  génomique:   puce   de  génotypage,   puce d'expression, RNA-­seq, DNA-­‐eq en génomes complets et en éxomes),\r\n2 assurer la liaison avec les centres de génomique (académiques  ou privés) réalisant le séquençage haut-­‐débit,\r\n3 effectuer l'analyse des données de séquence transmises par ces centres de manière à en extraire l'information  (variants  somatiques  et  structuraux,  nombre  de  copie,  niveaux  d'expression  en RNA-­‐seq) utile aux équipes biomédicales.\r\n \r\nPour  remplir  sa  mission,  la  plateforme  a  développé  une  application  Internet  permettant  d'assurer  la gestion  de grands  projets  multicentriques  en toute  transparence  pour  les collaborateurs.  Elle  a mis en place  des  procédures  de  contrôle  qualité  des  échantillons  à  analyser.  Elle  dessine  et  automatise  des pipelines d'analyse pour les données de génomique qu'elle reçoit.",
            "expertise": [],
            "expertise_description": "",
            "linkCovid19": "",
            "homepage": "http://www.synergielyoncancer.fr/",
            "unitId": "",
            "address": "28 rue Laënnec\r\nFondation Synergie Lyon Cancer, Bât. Cheney D\r\n69008 Lyon\r\nFrance",
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            "description": "IMGT®, the international ImMunoGeneTics information system®, is the international reference in immunogenetics and immunoinformatics, created in 1989 at the University of Montpellier and the CNRS. IMGT® is a high-quality integrated knowledge resource specialized in the immunoglobulins (IG) or antibodies, T cell receptors (TR), major histocompatibility (MH) of human and other vertebrate species, and in the immunoglobulin superfamily (IgSF), MH superfamily (MhSF) and related proteins of the immune system (RPI) of vertebrates and invertebrates.",
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            "expertise_description": "The IMGT platform specializes in the nomenclature and classification of immunoglobulin genes and T cell receptors, facilitating research on the immune response. It offers databases and tools along three axes: genomics, the expressed repertoire, and proteins (including crystallographic structure).",
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            "homepage": "https://www.imgt.org/",
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            "country": "France",
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                "IMGT_mAb-DB",
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            "name": "PRABI-PFGT",
            "logo_url": "https://www.crcl.fr/app/uploads/2021/01/logo.svg",
            "description": "La plateforme de Bioinformatique \"Gilles Thomas\", située au Centre Léon Bérard (CLB), a été initiée en 2009 par le Pr. Gilles Thomas pour favoriser l'exploitation de quantités massives de données de séquençage en génomique du cancer. L'équipe est composée de 11 bioinformaticiens et biostatisticiens travaillant sous la direction scientifique d'Alain Viari (Inria). Elle fournit une expertise multidisciplinaire, de la gestion des données à l'interprétation biologique, pour soutenir un large spectre de collaborations allant de la recherche fondamentale aux projets translationnels et aux activités de diagnostic clinique.",
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            "address": "28 Rue Laennec",
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            "expertise_description": "The Migale bioinformatics platform provides access to a computing infrastructure for bioinformatics and biostatistics. Migale organizes an annual training cycle in bioinformatics through practice. It also supports the community in its research projects by providing expertise in the development of bioinformatics tools and the analysis of genomic or metagenomic data.",
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        {
            "id": 21,
            "name": "PRABI-Lyon-Gerland",
            "logo_url": null,
            "description": "Le PRABI-Gerland https://prabi.ibcp.fr localisé à l'IBCP développe les bases de données dans le domaine infectieux, les méthodes de prédiction et d'optimisation des structures 3D de protéines ainsi que les outils et services s'y rapportant. Dans le domaine des services la spécificité de la PF est la bioinformatique structurale (Modélisation moléculaire, prédiction de structure) introduite sur Lyon dès 1986 (il y a 25 ans avant même que le mot n'existe...). Dans ce domaine, l’activité proposée par le PRABI-Gerland s’appuie sur les expertises suivantes:\r\n Prédiction de structure de protéines [G. Deléage]\r\n Modélisation moléculaire [E. Bettler, G. Deléage, R. Terreux]\r\n Intégration de méthodes et serveurs Web [C. Combet, G Deléage]\r\n Serveur Web 3D [E. Bettler, G. Deléage]\r\n Drug design et QSAR (R. Terreux, J.A. Chemelle)\r\n \r\nMots clefs: Bioinformatique structurale, Prédiction de structure, Base de données structurales, Analyse de séquences, Modélisation moléculaire, docking moléculaire.\r\n \r\nPrincipaux sites web: https://prabi.ibcp.fr (site en cours de refonte)\r\n https://geno3d-prabi.ibcp.fr/\r\n https://npsa-prabi.ibcp.fr/\r\n http://sumo-pbil.ibcp.fr\r\n http://espript.ibcp.fr\r\n http://endscript.ibcp.fr\r\nMéthodes de prédiction des structures secondaires de protéines.\r\nPlusieurs méthodes originales ont été développées, Self Optimized Prediction Method (SOPM), génère automatiquement à partir de cette base de donnée, une \"sous-base\" rassemblant les 60 à 80 protéines les plus homologues ou appartenant à la même classe structurale que la protéine\r\nétudiée. En effet, des protéines homologues ont généralement une structure assez proche (30% d'identité indique une architecture semblable). Après une phase d'apprentissage automatique sur cette \"sous-base\", en particulier d'optimisation des paramètres, la prédiction de la structure de la protéine est réalisée. La version SOPMA tire bénéfice des alignements multiples. La méthode MLRC combine les réseaux de neurones avec la méthode SOPMA.\r\n [SOPMA] Self optimised Prediction Method (1995)\r\n [SOPM] Self optimised Prediction Method (1994)\r\n [DPM] Double prediction Method (1987)\r\n [MLRC] Multivariate Linear Regression Combination (1999)\r\n [AMPHIPASEEK] Prediction of membrane anchor helical peptides (2006)\r\n \r\nIntégration de methodes- WebicielsServeur NPS@\r\nLe PRABI Gerland a développé le premier serveur de mail Français pour la prédiction de structures secondaires de protéines (80 000 prédictions en tout). Ensuite ces méthodes ont été intégrées dans [NPS@ 2000]. Le serveur est actuellement dans sa version 3. Dans le cadre de RENABI-IFB, ce serveur généraliste de séquences couplé aux prédictions de structures sera mis à jour en termes d’ergonomie, d’interface et de conception. Mise à disposition d’outils et de services en ligne correspondant aux domaines d’expertise du laboratoire d’accueil de la PF.\r\n \r\nServeur Web ESPript/ENDscript\r\nA partir d’une protéine de structure connue (code ou fichier PDB), le serveur ENDscript produit, en quelques secondes et de manière automatisée, plusieurs illustrations téléchargeables dans des formats usuels (PostScript, PDF, PNG et TIFF) :\r\n1/ Une première figure, générée par le logiciel ESPript, présente la séquence de la protéine d’intérêt agrémentée de ses éléments de structure secondaire, de l’accessibilité au solvant et de l’hydropathie par résidu. Si disponibles, sont aussi représentés les contacts cristallographiques et non-cristallographiques protéine/protéine et/ou protéine/ligand ainsi que les résidus impliqués dans des ponts disulfures.\r\n2/ Une seconde figure ESPript montre, en plus des informations précédentes, un alignement multiple de séquences des protéines homologues coloré en fonction de la conservation des résidus et agrémenté des éléments de structure secondaire de ces dernières si leurs structures sont connues. \r\n3/ Deux représentations 3D interactives visualisables par le logiciel PyMOL : a) une représentation en ruban, colorée en fonction de la conservation de séquence. b) une représentation en tube dont le diamètre est proportionnel à la déviation structurale (rmsd) entre la protéine d’intérêt et les protéines homologues de structure connue. De plus, si disponible, peuvent être affichés : l’assemblage de l’unité biologique, les modèles RMN multiples, les ligands et les résidus en contact avec ces derniers.\r\nLe serveur ESPript permet, en complément d’ENDscript ou de manière autonome, de représenter des alignements multiples de séquences avec la possibilité d’ajouter des marqueurs définis par l’utilisateur de manière à produire des figures facilitant l’analyse ou dédiées aux communications scientifiques.\r\n \r\nModélisation moléculaire\r\nUn serveur Web de modélisation moléculaire automatique de structure 3D de protéines appelé geno3D est disponible depsuis 2002 qui permet aux biologistes et biochimistes d'obtenir un modèle 3D de qualité si la séquence \"query\" présente plus de 35% d'identité avec une protéine de structure 3D connue. Le principe de cette modélisation consiste à appliquer les techniques de modélisation sous contraintes à la protéine à modéliser (de type RMN) à partir d'un jeu de contraintes calculées sur l'empreinte structurale. Plusieurs empreintes sont utilisables, le ligand (si présent) est replacé dans les modèles, 10 modèles sont générés. Les résultats sont proposés sous la forme d’une archive récupérable et les résultats sont conservés 8 jours sur le serveur. Ce serveur génère 100 modèles/mois. Un système intégré de modélisation moléculaire (MAGOS ) à grande échelle de protéomes entiers a été utilisé pour des protéomes de virus (modeome3D) et de plantes (arabidome3D).\r\n \r\nDocking et sites 3D- chemo-informatique\r\nUne méthode bioinformatique SUMO a été développée permettant de détecter des sites 3D fonctionnels communs à plusieurs protéines. 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