Researchers at the Johns Hopkins Bloomberg School of Public Health in Baltimore have developed new software, known as Myrna, to improve the speed at which scientists can analyze RNA sequencing data using cloud computing, according to an article published online in Genome Biology.
Faster, cost-effective analysis of gene expression could be a valuable tool in understanding the genetic causes of disease, the researchers stated.
To test Myrna, Ben Langmead, a research associate in the Bloomberg School's Department of Biostatistics, and colleagues Kasper Hansen, PhD, a postdoctoral fellow, and Jeffrey T. Leek, PhD, senior author of the study and assistant professor in the Department of Biostatistics, used the software to process a large collection of publicly available RNA sequencing data.
Processing time and storage space were rented from Amazon Web Services. According to the authors, Myrna calculated differential expression from 1.1 billion RNA sequencing reads in less than 2 hours at cost of about $66.
"Biological data in many experiments—from brain images to genomic sequences—can now be generated so quickly that it often takes many computers working simultaneously to perform statistical analyses," concluded the authors. "The cloud computing approach we developed for Myrna is one way that statisticians can quickly build different models to find the relevant patterns in sequencing data and connect them to different diseases. Although Myrna is designed to analyze next-generation sequencing reads, the idea of combining cloud computing with statistical modeling may also be useful for other experiments that generate massive amounts of data."
The Myrna software is available for free download here.