LOOKING FOR ASSISTANCE WITH ANALYZING AND INTERPRETING YOUR DATA?
AltraBio is a contract research company specializing in the analysis of biological and medical data using statistical methods and artificial intelligence.
Trusted globally, AltraBio serves as a research and development partner for leading companies and university hospitals across pharmaceuticals, medical devices, diagnostics, and dermato-cosmetics sectors.
How can we work together?
Partnership
Development of computational tools for data analysis in regional / national / international consortia.
Examples of current and completed projects:
Subcontracting
Data analysis for companies and university hospitals.
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Hundreds of completed projects
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Regular customers including top 10 pharmas and leaders in cosmetics
Funding
NEWS
March 2023
Best Biological & Medical Data Analysis Research Company 2023 – Western Europe
We are proud to have been awarded the title [...]
January 2023
XXXI Cosmet’in Lyon Skin Science Days
AltraBio will be present at the XXXI Cosmet'in Lyon Skin [...]
September 2021
CYTO Virtual Interactive Event
Why waste your time analyzing cytometry data manually when gating [...]
LATEST 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 Journal Article
In: 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}
}
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.
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 Journal Article
In: iScience, vol. 27, no. 4, pp. 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}
}
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.
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
In: Transfusion Clinique et Biologique, vol. 30, iss. S1, pp. 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}
}