In a study published in BMC Bioinformatics, researchers from Baylor College of Medicine’s Human Genome Sequencing Center, along with Oak Ridge National Laboratory, DNAnexus and the Human Genetics Center at the University of Texas Health Science Center, have developed a novel hybrid computational strategy to address the growing need for scalable, cost effective and real time variant calling of whole genome sequencing data.
This new strategy has proven successful in analyzing an unprecedented set of 5,000 samples, which constitute a critical part for the international consortia efforts called CHARGE (The Cohorts for Heart and Aging Research in Genomic Epidemiology), aiming to identify genetic culprits for a number of common chronic diseases.
“The demand for and the sheer size of sequencing is advancing more quickly than the downstream analytical technologies can adapt.” said Dr. Zhuoyi Huang, the leading author and a postdoctoral fellow with Baylor’s Human Genome Sequencing Center.
“We have created a strategy that is highly scalable for increasingly larger samples, and have developed an understanding of best practices for the process, which can be replicated by other research institutions,” said Dr. Navin Rustagi, the other leading author on the paper, also a postdoctoral fellow with the Human Genome Sequencing Center at Baylor.