Analyses de Haute Qualité et Approche Collaborative
AltraBio utilise son expertise reconnue en bioinformatique, biostatistique et biologie pour offrir des services d’analyse et d’interprétation de divers types de données omiques (génomique, épigénomique, transcriptomique, protéomique, etc.).
Notre équipe collabore étroitement avec les clients et partenaires pour chaque projet afin de garantir que leurs objectifs sont atteints. Cette approche collaborative assure que nos analyses sont alignées avec vos objectifs de recherche.
Expertise en Biostatistique et Bioinformatique
Avant de réaliser des analyses différentielles, nous mettons en œuvre diverses méthodes pour évaluer la qualité des données et leur cohérence avec la conception expérimentale. Nous traitons les valeurs aberrantes et les effets non liés à la conception, garantissant ainsi la pertinence de l’analyse.
Les conceptions expérimentales impliquent souvent plusieurs facteurs tels que le donneur, le type cellulaire, le traitement, la dose et les points temporels. Pour répondre aux questions biologiques de l’étude, AltraBio identifie le modèle statistique le plus approprié, en tenant compte des conceptions appariées, des corrections d’effets de lot, de l’estimation de facteurs cachés et de la pondération des valeurs aberrantes.
AltraBio excelle dans l’intégration de divers types de données (multi-omiques, cytométrie, données médicales, etc.). Nous utilisons des techniques d’apprentissage automatique supervisé et non supervisé pour des applications telles que l’identification de biomarqueurs, la classification et les modèles prédictifs pour le diagnostic ou la réponse au traitement. Nos clients bénéficient de notre maîtrise des algorithmes d’apprentissage automatique de pointe.
Expertise en Biologie
Nous identifions les processus biologiques et les voies par le biais de méthodes complémentaires d’enrichissement de catégories fonctionnelles. Ces résultats automatisés sont ensuite examinés pour évaluer leur pertinence dans le contexte biologique de l’étude.
Au-delà de la fourniture de listes de molécules et de voies biologiques, AltraBio extrait des informations significatives. Lors de la phase d’interprétation, nous considérons les questions biologiques initiales et évaluons les résultats en intégrant les connaissances biologiques disponibles dans la littérature scientifique et les bases de données. Notre objectif est de comprendre les mécanismes biologiques en jeu et de formuler de nouvelles hypothèses à valider.
Rapports
Tout le travail réalisé est résumé dans un rapport complet, fourni à nos clients et expliqué lors d’une visioconférence. Cet échange permet de clarifier les approches méthodologiques choisies et leurs résultats, assurant une compréhension optimale des données.
Les résultats des analyses statistiques sont également accessibles via l’interface web WikiBioPath. Cette plateforme offre à nos clients un ensemble d’outils de visualisation et d’analyse pour continuer à explorer leurs données. Ils peuvent facilement visualiser des volcano plots, générer de nouvelles heat maps, effectuer des PCA et réaliser des analyses d’enrichissement sur des sélections de gènes.
Testimonials
« Even in the age of generative AI, Altrabio’s two decades of expertise in maths, stats, biology, and medical science remain invaluable. They don’t just talk, they do. No flashy marketing, no inflated costs, just solid, thoughtful work from study design to actionable insights. A trusted partner, for twenty years, in a world full of noise. Highly recommend working with them to make real sense of your complex biomedical and omics data. »
Nos Publications
2014
Samarut, Eric; Gaudin, Cyril; Hughes, Sandrine; Gillet, Benjamin; de Bernard, Simon; Jouve, Pierre-Emmanuel; Buffat, Laurent; Allot, Alexis; Lecompte, Odile; Berekelya, Liubov; Rochette-Egly, Cécile; Laudet, Vincent
Retinoic acid receptor subtype-specific transcriptotypes in the early zebrafish embryo Article de journal
Dans: Mol Endocrinol, vol. 28, no. 2, p. 260–272, 2014, ISSN: 1944-9917.
@article{pmid24422634,
title = {Retinoic acid receptor subtype-specific transcriptotypes in the early zebrafish embryo},
author = {Eric Samarut and Cyril Gaudin and Sandrine Hughes and Benjamin Gillet and Simon de Bernard and Pierre-Emmanuel Jouve and Laurent Buffat and Alexis Allot and Odile Lecompte and Liubov Berekelya and Cécile Rochette-Egly and Vincent Laudet},
doi = {10.1210/me.2013-1358},
issn = {1944-9917},
year = {2014},
date = {2014-02-01},
urldate = {2014-02-01},
journal = {Mol Endocrinol},
volume = {28},
number = {2},
pages = {260--272},
abstract = {Retinoic acid (RA) controls many aspects of embryonic development by binding to specific receptors (retinoic acid receptors [RARs]) that regulate complex transcriptional networks. Three different RAR subtypes are present in vertebrates and play both common and specific roles in transducing RA signaling. Specific activities of each receptor subtype can be correlated with its exclusive expression pattern, whereas shared activities between different subtypes are generally assimilated to functional redundancy. However, the question remains whether some subtype-specific activity still exists in regions or organs coexpressing multiple RAR subtypes. We tackled this issue at the transcriptional level using early zebrafish embryo as a model. Using morpholino knockdown, we specifically invalidated the zebrafish endogenous RAR subtypes in an in vivo context. After building up a list of RA-responsive genes in the zebrafish gastrula through a whole-transcriptome analysis, we compared this panel of genes with those that still respond to RA in embryos lacking one or another RAR subtype. Our work reveals that RAR subtypes do not have fully redundant functions at the transcriptional level but can transduce RA signal in a subtype-specific fashion. As a result, we define RAR subtype-specific transcriptotypes that correspond to repertoires of genes activated by different RAR subtypes. Finally, we found genes of the RA pathway (cyp26a1, raraa) the regulation of which by RA is highly robust and can even resist the knockdown of all RARs. This suggests that RA-responsive genes are differentially sensitive to alterations in the RA pathway and, in particular, cyp26a1 and raraa are under a high pressure to maintain signaling integrity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Faugaret, Delphine; Amara, Amira Ben; Alingrin, Julie; Daumas, Aurélie; Delaby, Amélie; Lépolard, Catherine; Raoult, Didier; Textoris, Julien; Mège, Jean-Louis
Granulomatous response to Coxiella burnetii, the agent of Q fever: the lessons from gene expression analysis Article de journal
Dans: Front Cell Infect Microbiol, vol. 4, p. 172, 2014, ISSN: 2235-2988.
@article{pmid25566510,
title = {Granulomatous response to Coxiella burnetii, the agent of Q fever: the lessons from gene expression analysis},
author = {Delphine Faugaret and Amira Ben Amara and Julie Alingrin and Aurélie Daumas and Amélie Delaby and Catherine Lépolard and Didier Raoult and Julien Textoris and Jean-Louis Mège},
doi = {10.3389/fcimb.2014.00172},
issn = {2235-2988},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Front Cell Infect Microbiol},
volume = {4},
pages = {172},
abstract = {The formation of granulomas is associated with the resolution of Q fever, a zoonosis due to Coxiella burnetii; however the molecular mechanisms of granuloma formation remain poorly understood. We generated human granulomas with peripheral blood mononuclear cells (PBMCs) and beads coated with C. burnetii, using BCG extracts as controls. A microarray analysis showed dramatic changes in gene expression in granuloma cells of which more than 50% were commonly modulated genes in response to C. burnetii and BCG. They included M1-related genes and genes related to chemotaxis. The inhibition of the chemokines, CCL2 and CCL5, directly interfered with granuloma formation. C. burnetii granulomas also expressed a specific transcriptional profile that was essentially enriched in genes associated with type I interferon response. Our results showed that granuloma formation is associated with a core of transcriptional response based on inflammatory genes. The specific granulomatous response to C. burnetii is characterized by the activation of type 1 interferon pathway.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2013
Altintas, Dogus Murat; Allioli, Nathalie; Decaussin, Myriam; de Bernard, Simon; Ruffion, Alain; Samarut, Jacques; Vlaeminck-Guillem, Virginie
Differentially expressed androgen-regulated genes in androgen-sensitive tissues reveal potential biomarkers of early prostate cancer Article de journal
Dans: PLoS One, vol. 8, no. 6, p. e66278, 2013, ISSN: 1932-6203.
@article{pmid23840433,
title = {Differentially expressed androgen-regulated genes in androgen-sensitive tissues reveal potential biomarkers of early prostate cancer},
author = {Dogus Murat Altintas and Nathalie Allioli and Myriam Decaussin and Simon de Bernard and Alain Ruffion and Jacques Samarut and Virginie Vlaeminck-Guillem},
doi = {10.1371/journal.pone.0066278},
issn = {1932-6203},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {PLoS One},
volume = {8},
number = {6},
pages = {e66278},
abstract = {BACKGROUND: Several data favor androgen receptor implication in prostate cancer initiation through the induction of several gene activation programs. The aim of the study is to identify potential biomarkers for early diagnosis of prostate cancer (PCa) among androgen-regulated genes (ARG) and to evaluate comparative expression of these genes in normal prostate and normal prostate-related androgen-sensitive tissues that do not (or rarely) give rise to cancer.
METHODS: ARG were selected in non-neoplastic adult human prostatic epithelial RWPE-1 cells stably expressing an exogenous human androgen receptor, using RNA-microarrays and validation by qRT-PCR. Expression of 48 preselected genes was quantified in tissue samples (seminal vesicles, prostate transitional zones and prostate cancers, benign prostatic hypertrophy obtained from surgical specimens) using TaqMan® low-density arrays. The diagnostic performances of these potential biomarkers were compared to that of genes known to be associated with PCa (i.e. PCA3 and DLX1).
RESULTS AND DISCUSSION: By crossing expression studies in 26 matched PCa and normal prostate transitional zone samples, and 35 matched seminal vesicle and PCa samples, 14 genes were identified. Similarly, 9 genes were overexpressed in 15 benign prostatic hypertrophy samples, as compared to PCa samples. Overall, we selected 8 genes of interest to evaluate their diagnostic performances in comparison with that of PCA3 and DLX1. Among them, 3 genes: CRYAB, KCNMA1 and SDPR, were overexpressed in all 3 reference non-cancerous tissues. The areas under ROC curves of these genes reached those of PCA3 (0.91) and DLX1 (0.94).
CONCLUSIONS: We identified ARG with reduced expression in PCa and with significant diagnostic values for discriminating between cancerous and non-cancerous prostatic tissues, similar that of PCA3. Given their expression pattern, they could be considered as potentially protective against prostate cancer. Moreover, they could be complementary to known genes overexpressed in PCa and included along with them in multiplex diagnostic tools.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
METHODS: ARG were selected in non-neoplastic adult human prostatic epithelial RWPE-1 cells stably expressing an exogenous human androgen receptor, using RNA-microarrays and validation by qRT-PCR. Expression of 48 preselected genes was quantified in tissue samples (seminal vesicles, prostate transitional zones and prostate cancers, benign prostatic hypertrophy obtained from surgical specimens) using TaqMan® low-density arrays. The diagnostic performances of these potential biomarkers were compared to that of genes known to be associated with PCa (i.e. PCA3 and DLX1).
RESULTS AND DISCUSSION: By crossing expression studies in 26 matched PCa and normal prostate transitional zone samples, and 35 matched seminal vesicle and PCa samples, 14 genes were identified. Similarly, 9 genes were overexpressed in 15 benign prostatic hypertrophy samples, as compared to PCa samples. Overall, we selected 8 genes of interest to evaluate their diagnostic performances in comparison with that of PCA3 and DLX1. Among them, 3 genes: CRYAB, KCNMA1 and SDPR, were overexpressed in all 3 reference non-cancerous tissues. The areas under ROC curves of these genes reached those of PCA3 (0.91) and DLX1 (0.94).
CONCLUSIONS: We identified ARG with reduced expression in PCa and with significant diagnostic values for discriminating between cancerous and non-cancerous prostatic tissues, similar that of PCA3. Given their expression pattern, they could be considered as potentially protective against prostate cancer. Moreover, they could be complementary to known genes overexpressed in PCa and included along with them in multiplex diagnostic tools.