RURO and Ayrris Announce Partnership
News Aug 07, 2013
RURO and Appistry have announced their partnership to deliver integrated solutions for Big-Data Lab Management for Production-Scale NGS.
Automation is a fact of life for modern labs. But effectively managing the increasingly ubiquitous handoffs between humans, instruments, and computers requires ways to expedite and support both the physical work performed in the lab and the associated digital tasks performed by a lab’s computational workers. This is particularly important for labs seeking to provide results and diagnostic insights from data-intensive techniques such as NGS.
The winning combination of RURO’s software used in a variety of laboratory settings and Appistry’s Ayrris big-data computing pipeline creates the data-aware, computationally conscious lab environments.
Smart integration of the Ayrris with LIMS 24/7 sets new standards for translational medicine. Customers using the joint solution will gain a seamless transition from NGS laboratory workflows into a computational pipeline and back.
It’s a logical integration given the influx of data from NGS. RURO’s LIMS 24/7 is a comprehensive, easy-to-implement laboratory information management system that tracks data associated with all administrative and wet lab processes, from receipt and preparation of samples through running libraries on sequencers. But a LIMS can’t track and expedite data processing and analysis steps, and that’s where Ayrris comes in. Sitting between sequencers and the applications that interpret and store data, Ayrris provides the key big data capabilities labs need to run customer’s pipelines at scale.
Laboratories seeking to implement clinical NGS will gain a continuous pipeline that makes collecting, storing, managing, and routing data simple, reliable, and efficient.
Integrating a LIMS with a robust big-data environment enables laboratories to:
• Establish and maintain a full chain of custody for samples and associated data across the experimental life cycle.
• Standardize physical and digital processes to achieve compliance with regulations such as CLIA, HIPAA or Safe Harbor.
• Create production-grade workflows necessary for running defined lab workflows at scale.
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
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