Texas Genomics Core Alliance helps accelerate your research with optimized workflow pipelines from sample preparation to generation of results.
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Texas A&M University and The University of Texas at Arlington have established The Texas Genomics Core Alliance to increase access and decrease costs to cutting-edge, high-throughput genomics sequencing technologies.
With vast expertise spanning two best public research universities in Texas, you can be rest assured that your experiment is in good hands. We will strive to assist you at every step of the way from consulting regarding sample preparation.
Our motto is to not only provide you the best expertise in sequencing your samples but at quick turn around time.
With vast expertise in developing custom algorithms for high throughput data analysis and expertise in deploying cutting-edge software solutions in high-performance computing environments, you have the opportunity to take advantage of our analysis pipelines tailored specifically for your data analysis needs.
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“By reducing costs and speeding up the process we can make genomics into a commodity and enhance innovation across the biotech, biomedical and industrial sectors,” said Jon Weidanz, UTA associate vice president for research and founding director UTA’s North Texas Genome Center.
Dr. Jon Widanz
Director, North Texas Genome Center
“Collaborations such as this will allow Texas A&M to continue raising the bar in human, animal and plant sciences at the state, national and global levels,” said Dr. David Threadgill, director of Texas A&M’s Institute for Genomics Sciences.
Dr. David Threadgill
Director, Texas A&M Institute for Genome Sciences and Society
The alliance, signed by UTA’s North Texas Genome Center and Texas A&M’s Institute for Genome Sciences and Society, brings together the expertise and resources for large-scale genomic sample preparation and bioinformatics analysis at A&M with the massive sequencing capacity housed at UTA. This partnership will increase sequencing capabilities available to investigators at both institutions while decreasing the cost and time required for data generation.