Carnegie Mellon University
March 09, 2017

Computational Method Makes Gene Expression Analyses More Accurate

Detects technical biases that otherwise confound test results

By Byron Spice

Carl Kingsford
Carl Kingsford

A new computational method can improve the accuracy of gene expression analyses, which are increasingly used to diagnose and monitor cancers and are a major tool for basic biological research.

Researchers from Carnegie Mellon University, Stony Brook University and Dana-Farber Cancer Institute said their method, called Salmon, corrects for the technical biases that are known to occur during RNA sequencing, or RNA-seq, the leading method for estimating gene expression. It operates at similar speeds as other fast methods — a critical factor as these tests are growing more common and numerous.

Their report was published online March 6 by the journal Nature Methods. Carl Kingsford, associate professor in CMU's Computational Biology Department, said the Salmon source code is freely available online and has been downloaded by thousands of users.          

"Salmon provides a much richer model of the RNA-seq experiment and of the possible biases that are known to occur during sequencing," Kingsford said. This is important, he added, because this technique is increasingly used for classifying diseases and their subtypes, understanding gene expression changes during development and tracking the progression of cancer.

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 efficiently 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 technology for producing these snapshots of gene activity. But depending on the tissue being analyzed and the way each sample is prepared, various experimental biases can occur and cause RNA-seq "reads" to be over- or under-sampled from various genes, Kingsford said.

"Though we know many of the kinds of biases that can occur, modeling them has to occur on a sample-by-sample basis," he added. "And if you have to build a complicated bias model using traditional methods, it takes a really long time."

The researchers named the method after a fish famous for swimming upstream because it employs an algorithm that can estimate the effect of biases and the expression level of genes as experimental data streams by.

"In that way, it is able to build up a rich bias model and do so approximately as fast as other fast analysis tools," Kingsford said.

The research was led by Kingsford and Rob Patro, assistant professor of computer science at Stony Brook. The research team also included Geet Duggal of DNANexus, who worked on this project as a post-doctoral researcher at CMU, and Michael I. Love and Rafael A. Irizarry, biostatisticians at Dana-Farber and the Harvard T.H. Chan School of Public Health. Love has since joined the University of North Carolina-Chapel Hill as an assistant professor of biostatistics.