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.
Analytical Tool Predicts Disease-Causing GenesNews
Predicting genes that can cause disease due to the production of truncated or altered proteins that take on a new or different function, rather than those that lose their function, is now possible thanks to an international team of researchers that has developed a new analytical tool to effectively and efficiently predict such candidate genes.
Researchers Move Closer to Completely Optical Artificial Neural NetworkNews
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.
Big Data Study Targets Genomic Dark Matter from Ocean Floor to Gut FloraNews
An international team led by computational biologist Fran Supek at IRB Barcelona develop a machine learning method to predict unknown gene functions of microbes.The system examines and compares ‘big data’ available on the metagenomes of human and environmental microbiomes.READ MORE