BESOIN D’AIDE POUR ANALYSER ET INTERPRÉTER VOS DONNÉES ?
AltraBio est une société de recherche contractuelle spécialisée dans l’analyse de données biologiques et médicales grâce à l’utilisation de méthodes statistiques et d’intelligence artificielle.
Elle est reconnue mondialement comme un partenaire de confiance pour les projets de recherche et développement menés par de grandes entreprises et des hôpitaux universitaires de premier plan évoluant dans divers secteurs tels que les produits pharmaceutiques, les dispositifs médicaux, le diagnostic et les dermato-cosmétiques.
COMMENT TRAVAILLER ENSEMBLE?
Partenariat
Développement d’outils informatiques pour l’analyse de données au sein de consortiums régionaux / nationaux / internationaux.
Exemples de projets réalisés ou en cours:
Sous-traitance
Analyse de données pour des entreprises ou des instituts hospitalo-universitaires.
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Des centaines de projets réalisés
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Des clients réguliers incluant les plus grandes pharmas et les leaders de la cosmétique
FINANCEMENTS
NOUVELLES
mai 2024
18e WRIB
🔬 Nous sommes ravis d'annoncer notre participation au 18e [...]
avril 2024
CYTO 2024
🔬 Nous sommes ravis d'annoncer notre participation au congrès [...]
janvier 2024
Colloque I3M
Nous sommes ravis d'annoncer notre présence au prochain colloque [...]
novembre 2023
Immunotherapies & Innovations for Infectious Diseases
AltraBio est ravi d'annoncer sa présence au prochain I4ID Congress [...]
DERNIERES PUBLICATIONS
2024
Ribeiro, Sara; Chaumet, Guillaume; Alves, Karine; Nourikyan, Julien; Shi, Lei; Lavergne, Jean-Pierre; Mijakovic, Ivan; de Bernard, Simon; Buffat, Laurent
BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata Article de journal
Dans: Pathogens, vol. 13, no. 8, 2024, ISSN: 2076-0817.
@article{pmid39204272,
title = {BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata},
author = {Sara Ribeiro and Guillaume Chaumet and Karine Alves and Julien Nourikyan and Lei Shi and Jean-Pierre Lavergne and Ivan Mijakovic and Simon de Bernard and Laurent Buffat},
doi = {10.3390/pathogens13080672},
issn = {2076-0817},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {Pathogens},
volume = {13},
number = {8},
abstract = {The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Shaoying; Prieux, Margaux; de Bernard, Simon; Dubois, Maxence; Laubreton, Daphne; Djebali, Sophia; Zala, Manon; Arpin, Christophe; Genestier, Laurent; Leverrier, Yann; Gandrillon, Olivier; Crauste, Fabien; Jiang, Wenzheng; Marvel, Jacqueline
Exogenous IL-2 delays memory precursors generation and is essential for enhancing memory cells effector functions Article de journal
Dans: iScience, vol. 27, no. 4, p. 109411, 2024, ISSN: 2589-0042.
@article{pmid38510150,
title = {Exogenous IL-2 delays memory precursors generation and is essential for enhancing memory cells effector functions},
author = {Shaoying Wang and Margaux Prieux and Simon de Bernard and Maxence Dubois and Daphne Laubreton and Sophia Djebali and Manon Zala and Christophe Arpin and Laurent Genestier and Yann Leverrier and Olivier Gandrillon and Fabien Crauste and Wenzheng Jiang and Jacqueline Marvel},
doi = {10.1016/j.isci.2024.109411},
issn = {2589-0042},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {iScience},
volume = {27},
number = {4},
pages = {109411},
abstract = {To investigate the impact of paracrine IL-2 signals on memory precursor (MP) cell differentiation, we activated CD8 T cell in the presence or absence of exogenous IL-2 (ex-IL-2). We assessed memory differentiation by transferring these cells into virus-infected mice. Both conditions generated CD8 T cells that participate in the ongoing response and gave rise to similar memory cells. Nevertheless, when transferred into a naive host, T cells activated with ex-IL-2 generated a higher frequency of memory cells displaying increased functional memory traits. Single-cell RNA-seq analysis indicated that without ex-IL-2, cells rapidly acquire an MP signature, while in its presence they adopted an effector signature. This was confirmed at the protein level and in a functional assay. Overall, ex-IL-2 delays the transition into MP cells, allowing the acquisition of effector functions that become imprinted in their progeny. These findings may help to optimize the generation of therapeutic T cells.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Cognasse, Fabrice; Nguyen, Kim Anh; Heestermans, Marco; Arthaud, Charles-Antoine; Eyraud, Marie-Ange; Prier, Amélie; Bernard, Simon De; Nourikyan, Julien; Duchez, Anne-Claire; Avril, Stéphane; Garraud, Olivier; Hamzeh-Cognasse, Hind
P-228 Les modèles mathématiques peuvent prédire l'activité des plaquettes humaines et les expressions protéiques en réponse à diverses stimulations Article de journal
Dans: Transfusion Clinique et Biologique, vol. 30, iss. S1, p. S147, 2023.
@article{nokey,
title = {P-228 Les modèles mathématiques peuvent prédire l'activité des plaquettes humaines et les expressions protéiques en réponse à diverses stimulations},
author = {Fabrice Cognasse and Kim Anh Nguyen and Marco Heestermans and Charles-Antoine Arthaud and Marie-Ange Eyraud and Amélie Prier and Simon De Bernard and Julien Nourikyan and Anne-Claire Duchez and Stéphane Avril and Olivier Garraud and Hind Hamzeh-Cognasse},
doi = {10.1016/j.tracli.2023.09.272},
year = {2023},
date = {2023-11-10},
urldate = {2023-11-10},
journal = {Transfusion Clinique et Biologique},
volume = {30},
issue = {S1},
pages = {S147},
keywords = {},
pubstate = {published},
tppubtype = {article}
}