Computational Method Dramatically Speeds Up Estimates of Gene Expression

As gene expression analysis grows in importance for both basic researchers and medical practitioners, researchers at Carnegie Mellon University and the University of Maryland have developed a new computational method that dramatically speeds up estimates of gene activity from RNA sequencing (RNA-seq) data.

With the new method, dubbed Sailfish after the famously speedy fish, estimates of gene expression that previously took many hours can be completed in a few minutes, with accuracy that equals or exceeds previous methods. The researchers’ report was published online April 20 by the journal Nature Biotechnology.

Gigantic repositories of RNA-seq data now exist, making it possible to re-analyze experiments in light of new discoveries. “But 15 hours a pop really starts to add up, particularly if you want to look at 100 experiments,” said Carl Kingsford, associate professor in CMU’s Lane Center for Computational Biology. “With Sailfish, we can give researchers everything they got from previous methods, but faster.”

RNA-seq is a leading method for producing snapshots of gene expression; in genomic medicine, it has proven particularly useful in analyzing certain cancers.

The RNA-seq process results in short sequences of RNA, called “reads.” In previous methods, the RNA molecules from which they originated could be identified and measured only by painstakingly mapping these reads to their original positions in the larger molecules.

But Kingsford, working with Rob Patro of the Lane Center, and Stephen M. Mount, an associate professor in UMD's Department of Cell Biology and Molecular Genetics, found that the time-consuming mapping step could be eliminated. Instead, they found they could allocate parts of the reads to different types of RNA molecules, much as if each read acted as several votes for one molecule or another.

Sailfish can complete its RNA analysis 20-30 times faster than previous methods.

Link to full article: http://www.cmu.edu/news/stories/archives/2014/april/april20_geneexpression.html