Signals Screening is an assay data analysis and management platform that enables scientists to transform raw instrument data into actionable compound ID lists in just a few clicks. Enable your teams to collaborate on a modern, scalable platform to address today's scientific and workflow needs but also drive unprecedented future updates.
See What's New in the latest Signals Screening Release
The Opportunity
All screening data tends to go through similar data processing workflows. Starting with raw data files generated by multiple instruments, transforming raw data into columnar data format, adding annotations and enriched with information about the plates/ wells etc. It’s then normalized using business rules followed by QA/QC and then is either extracted for reporting or published to the corporate data warehouse.
Despite a standard workflow, it is challenging to reproduce the research, or apply the workflow to new/ historical set of data in a consistent way and achieve the exact defined end points.
A platform built with future in mind.
Signals Screening is an intuitive, configurable, and flexible screening workflow processing engine on top of the unparalleled data visualization and analysis capabilities of TIBCO Spotfire. A single platform that allows data comparisons from different experiments and instruments increasing confidence that the results are real. Whether it is low throughput assay development/ optimization experiment or high throughout screening runs, Signals Screening processes and analyzes all data with equal ease and scalability.
Organizations leverage Signals Screening to
Data Formats Supported
Imaging-based phenotypic screening of cell-based disease models has become an indispensable tool for modern drug discovery. Despite the growing adoption of high-content screening (HCS), analyzing the complex imaging data produced by these systems can take weeks and typically requires handson programming by data scientists.
Recent advances in deep learning have enabled the possibility of automating these analyses. In this work we present a framework to analyze multiple image datasets with minimal tuning or optimization.
Recent times have demonstrated the need for digital transformation, often in harsh terms, including a loss of R&D productivity. As a result, R&D organizations in advanced chemical manufacturing must find ways to improve efficiency in an increasingly challenging environment. This white paper provides guidance on which technologies yield improved turnaround times, smarter questions, and faster answers.
Researchers are increasingly looking to 3D cell cultures, microtissues, and organoids to bridge the gap between 2D cell cultures and in vivo animal models. This whitepaper documents a streamlined procedure for getting the most information, as quickly as possible, using solutions from PerkinElmer.
