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Gene expression analysis is growing in importance for both basic researches and medical practitioners. However, the speed existing models provide is not expectedly enthusiastic yet. Addressing the same limitation, researchers have developed a new computational method that reportedly speeds up estimates of gene activity from RNA sequencing data. 

According to press release, the estimates of gene expression that previously took many hours can be completed in a few minutes with accuracy. 

Researchers credited the improvement in speed to gigantic repositories of RNA-seq data existing that made 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, an associate professor in CMU's Lane Center for Computational Biology in the press release. "With Sailfish, we can give researchers everything they got from previous methods, but faster."

Though an organism's genetic makeup is static, the activity of individual genes varies greatly over time, making gene expression an important factor in understanding how organisms work and what occurs during disease processes. Gene activity can't be measured directly, but can be inferred by monitoring RNA, the molecules that carry information from the genes for producing proteins and other cellular activities. RNA-seq is a leading method for producing these snapshots of gene expression; in genomic medicine, it has proven particularly useful in analyzing certain cancers, read the release. 

In previous methods, the RNA molecules from which they originated could be identified and measured only by painstakingly mapping the reads - short sequences of RNA - to their original positions in larger molecules. However, without the mapping step, the newly discovered model can complete its RNA analysis 20-30 times faster than previous methods, said researchers.

Researchers have published their findings online in the journal Nature Biotechnology.