Boehringer Ingelheim Chooses Genedata Selector for Cell Line Development and Cell Culture Optimization
News Mar 10, 2016
Genedata has announced that Boehringer Ingelheim has chosen Genedata SelectorTM as its global computational platform for conducting innovative research in genomics-based cell line development and cell culture optimization. Genedata has entered into a long-term strategic collaboration with Boehringer Ingelheim and will also provide bioinformatics consulting services to further optimize R&D operations in Process Sciences.
“Cell line development and cell culture optimization face a new era with the introduction of knowledge-driven genomics based on new technologies such as Next Generation Sequencing (NGS) and omics-based approaches,” said Dr. Harald Bradl, Director, Cell Culture and Process Sciences at Boehringer Ingelheim. “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 Selector was our obvious choice.”
“We are very excited that Boehringer Ingelheim has selected Genedata Selector for its innovative genomics-based research in cell line development and cell culture optimization,” said Dr. Othmar Pfannes, CEO of Genedata. “Genedata is committed to the continued development of Genedata Selector to make it the solution of choice for researchers to fully leverage the most advanced and innovative technologies used in genome-based research, and for organizations to maximize their return on investment in NGS and omics technologies.”
Knowledge-Based Decision Making and Streamlined Processes to Drive Efficiency
Genedata Selector is an enterprise-level genome knowledge management solution with tailored content for biopharmaceutical applications such as host cell line design, clone validation and the safety assessment of bioproducts. For example, the system provides key information for the identification of the optimized host cell line, the efficient prioritization and validation of engineering targets to improve protein production, or the detection of adventitious agents in the bioproduct.
Genedata Selector integrates genomic data from different cell lines and offers standardized and reproducible workflows for the processing and analysis of RNASeq data, e.g. for gene prediction and gene model refinement of proprietary cell lines. Differences between cell lines on the DNA, methylation, protein or pathway level can be elucidated easily using interactive analysis tools included in the system. Engineering targets maximizing functional performance can be efficiently identified and prioritized based on sophisticated regulation and signaling pathway analysis.
On top of providing innovative insights to cell line development and cell culture optimization, Genedata Selector streamlines R&D processes, thereby cutting costs and reducing development times. Genedata Selector’s integrated approach to global knowledge management facilitates collaboration among research groups and sites, with easy access to all data under one umbrella system.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.