World Leading. Forward Thinking.
Many of the world’s leading biopharmaceuticals, agricultural and industrial biotechs, and academic research institutions collaborate with Genedata. Here is a sampling.
We need to enhance our capacity to analyze large amounts of high-dimensional ‘omic data emerging from our rapidly growing number of translational and clinical research projects. We chose Genedata because it is the end-to-end enterprise software able to harmonize our dispersed data analysis pipelines.
Kimio Terao Department Manager of Clinical Pharmacology
We aimed to extend automation and further enhance the efficiency of our cell line, upstream and bioanalytical development operations, and quickly realized that Genedata addresses many of our requirements off-the-shelf, eliminating the need for expensive customization.
To optimally handle the complex data, we need a centralized genome knowledge management and analysis solution that addresses the interdisciplinary challenges in next-generation biotechnology innovations. Genedata was our obvious choice.
Harald Bradl, Ph.D. Director, Cell Culture and Process Sciences
Genedata enables us to integrate a wide variety of complex data generated by novel experimental workflows so we can gain a holistic molecular understanding of production cells with the goal of developing high performing bioproduction processes.
Ruth Wagner, Ph.D. Group Manager of Cell Line Engineering
After reviewing the options available to us, we concluded that Genedata Screener matches perfectly with our vision of being totally compatible with other digital innovation initiatives currently ongoing at Servier
Jean-Philippe Stephan, Ph.D. Director, Center of Excellence Pharmacological Screening, Compound Management and Biobanking
The Biologics Data Platform supports our scientists in a complex, multi-site environment by providing all the necessary information and tools to streamline the biologics lead generation and optimization process.
Heiner Apeler, Ph.D. Director of Molecular and Antibody Biology
Discover how Merck Serono implemented Genedata Expressionist® to develop novel and efficient MS-based methods for the analysis of charge-variants, glycoforms, and released glycans in biopharmaceuticals - resulting in time savings of up to 80% compared to conventional methods.
Pfizer chose the Genedata platform to serve as a central data backbone and application environment upon which they could build tools to flexibly address Pfizer-specific requirements and drive innovation. Today, the system is used by over 250 people in 15 groups located at six Pfizer R&D sites globally, supporting 200+ distinct projects.
The MorphoSys adoption of Genedata Biologics® as their end-to-end workflow platform significantly increased efficiencies across all R&D functions, including library generation, screening and selection, molecular biology, antibody screening, protein engineering, expression, purification, and analytics.
Using Genedata Expressionist®, scientists at Genmab established an automated MS-based high-throughput assay that provides qualitative and quantitative information on a DuoBody® bsAb preparation. Compared to the CEX-based process, data acquisition and processing times were reduced by 95% and the number of person hours dedicated to data processing was reduced by 70%.
Discover a new solution embedded within Genedata Screener® which allowed Janssen to rapidly and simultaneously analyze expression for 20 to 80 different genes per compound, measured in plate-based screens performed using the QuantiGene® RNA Assay.
AstraZeneca integrated Genedata Expressionist®, an MS analysis software, to build an end-to-end HT-MS analysis workflow and Genedata Screener®, an analysis platform for in-vitro screening. These integrations enabled a 7-fold decrease in analysis time, preparing AstraZeneca to expand use of their innovative technology.
The Genedata platform enables Roche to fully automate the analysis of lower throughput assays. They achieved end-to-end automation, including of the data analysis, allowing them to save significant time, improve data quality and result robustness.