Takara Bio, a global biotechnology company based in Shiga, Japan, has chosen Genedata Expressionist for Genomic Profiling as its key software platform for next-generation sequencing (NGS) data. Genedata, headquartered in Switzerland, is a provider of advanced software solutions for drug discovery and life science research.
The platform will be integrated with in-house and third-party data analysis tools and databases, providing a single integrated environment for fully automated multi-omics data processing, comprehensive data management and powerful statistical analysis. Genedata will be the solution provider for both business areas of Takara Bio: its biotechnology research in regenerative medicine and cell therapy and its contract development and manufacturing organization business.
Takara Bio has established one of the largest genome analysis centers in Asia and provides contract services to a large variety of industries and leading academic institutions by offering genomics (DNA-Seq.), transcriptomics (RNA-Seq.) and epigenomics (ChIP-Seq. and Methyl-Seq.) analysis services. Genedata Expressionist for Genomic Profiling will be utilized to automate and standardize the data processing and analysis of all NGS and microarray data. The highly scalable enterprise system can simultaneously analyze and visualize thousands of experiments in high throughput.
“We are committed to deliver innovative services and highest quality data to our customers. Genedata helps us to continue to deliver on our promise by providing a software platform which automates and manages NGS data analysis workflows for all relevant applications,” said Masanari Kitagawa, executive officer of Takara Bio. “In addition, the platform enables us to utilize the latest technology developments for our own internal research projects.”
One of the reasons Takara Bio chose the Genedata platform is its ability to integrate multi-omics data from internal and external data sources. The open and comprehensive software solution allows interdisciplinary teams to work together and to extract the most value from highly complex experimental datasets.
