New York Genome Institute Selects Exemplar LIMS to Manage Next Gen Sequencing Labs
News Mar 03, 2013
Exemplar LIMS™ has been selected as the primary lab management and electronic lab notebook (ELN) solution for NY Genome Center's laboratory operations. Sapio's LIMS system will initially track next generation sequencing workflows from the Illumina family of sequencers from request through analysis. Support for additional next gen sequencers will be added as needed.
Sapio's LIMS software will be deployed on Apple iPads™ using Exemplar LIMS for Tablets solution. This enables lab technicians to take the LIMS with them as they move around the laboratory, improving LIMS usability and lab technician efficiency. NY Genome Center will also be utilizing Exemplar's Materials Management capabilities for detailed tracking of reagent usage. NYGC will ultimately be processing samples for both research and clinical applications via a CLIA certified laboratory. Sapio's experience with implementing best practices for both research and clinical labs will accelerate the LIMS implementation in support of these NYGC objectives.
"The team at New York Genome Center has been impressed with the flexibility and scalability of Sapio's offerings," commented NYGC President & Scientific Director Robert B. Darnell, MD, PhD. "With Sapio we found an entrepreneurial partner that understands how important customer service and collaboration are to NYGC. We look forward to working with Sapio to design a solution for the genome center of the future."
"The leadership at NYGC has recognized the importance of next generation sequencing technology for both research and clinical applications and has assembled a top-notch team from top to bottom. We are both honored and enthused to be supporting the NYGC objectives with the ultimate goal of curing diseases," said Kevin Cramer, VP at Sapio Sciences. "We look forward to working with the NYGC team towards creating a world-class lab operation as they scale to be one of the largest NGS facilities in the North America."
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.
Towards Personalized Medicine: One Type of Data is Not EnoughNews
To understand the biology of diseased organs researchers use different types of molecular data. One of the biggest computational challenges at the moment is integrating these multiple data types. A new computational method jointly analyses different types of molecular data and disentangles the sources of disease variability to guide personalized treatment.READ MORE