Genedata Screener Adopted for all Plate-based Screening by Major Pharmas
News Jan 20, 2013
Genedata, a leading provider of advanced software solutions for drug discovery and life science research, has announced that more than half of the world's top 25 pharmaceutical companies have adopted Genedata Screener as their plate-based screening analysis platform. Also used by biotechnology companies, contract research organizations, and academic research institutions, Genedata Screener captures, analyzes, and manages all types of screening data on one platform. The system, which reduces data processing times by upwards to 80%, integrates with any plate-based instrument to support High, Medium and Low Throughput Screening, High Content Screening (HCS), Ion Channel Screening, Label-free (including thermal shift), and other assay technologies.
One Platform for All Plate-based Screening
The cost savings, productivity gains, and time efficiencies of a single-platform screening analysis system with built-in business logic for standardized and automated workflows are driving the accelerated adoption of Genedata Screener. Addressing the complete screening workflow, Genedata Screener scales from the smallest datasets with single read-outs per well to 1,000s of plates in 1536-well format with full kinetics resolution. Its intuitive data analysis environment enables very fast experiment set-up, data import and processing with set-up times reduced by more than 50% compared to other systems. Genedata Screener uniquely supports the complete range of:
Classic Plate-based Screening Instruments
Fluorescence Polarization, FRET
High Content Screening
Numeric well and cell results stored in files or databases
Images from local or central image storage
Off-the shelf support for
Molecular Devices MDC Store™ Data Management
openBIS for HCS
Perkin Elmer Columbus™ Image Data Management
Thermo Scientific Cellomics® Store
Time Series results as produced in
Ion Channel Screening
Thermal Shift Screening
Industry Adoption - Co-Development Drives Innovation
The system's data analysis capabilities are further advanced through co-development projects with leading pharmaceutical companies. Currently, the Genedata team is working with customers to have Genedata Screener support native processing for calculation of compound combination effects - such as synergy or antagonistic effects. Exploring compound combination effects is a rapidly expanding field in pharmaceutical and biotechnology research. Genedata Screener provides a standardized platform on which researchers can systematically, reliably and efficiently visualize and explore the scientific principles of compound combinations is critical to furthering innovation in drug and treatment discovery.
"Over the past two years, we have seen Genedata Screener's adoption more than double, making it the de-facto screening analysis standard at leading pharmaceutical companies," said Dr. Othmar Pfannes, CEO of Genedata. "The increased use of Genedata Screener is also due in part to our commitment to industry collaborations that drive innovative research. For example, our collaborations will develop the industry's first commercial platform to fully support quantification of drug combination effect as part of a complete interactive and multi-dimensional screening workflow."
Streaming Protocol Makes Gene Data Sharing Future-ProofNews
The Large Scale Genomics Work Stream of the Global Alliance for Genomics and Health (GA4GH) has announced eight new implementations of its htsget protocol, a standard released in October 2017 for accessing large-scale genomic sequencing data online that does not depend on file transfers. The protocol and interoperability testing are reported in a paper released online this week in the journal Bioinformatics.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.
Antarctic Worm and Machine Learning Help Identify Cerebral Palsy EarlierNews
A research team has released a study in the peer-reviewed journal BMC Bioinformatics showing that DNA methylation patterns in circulating blood cells can be used to help identify spastic cerebral palsy (CP) patients. The technique which makes use of machine learning, data science and even analysis of Antarctic worms, raises hopes for earlier targeted CP therapies.