Moore Foundation
grantee Carl Kingsford and colleagues at Carnegie Mellon University, Stony Brook University and Dana-Farber Cancer Institute have developed a new computational method to improve the accuracy of gene expression analyses. These analyses are a major tool for basic biological research.
The team's new method, called Salmon, takes into account the role of gene expression in understanding how organisms work, including what occurs during disease progression. 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.
"Salmon provides a much richer model of the RNA-seq experiment and of the possible biases that are known to occur during sequencing," said Carl Kingsford, an associate professor of computational biology at Carnegie Mellon and an
investigator through the foundation's
Data-Driven Discovery initiative.
RNA-seq is a leading technology for producing 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.
The
Salmon source code also operates at similar speeds as other fast methods, and is freely available online. The team's findings were published earlier this week in Nature Methods.
The researchers named the method after a fish famous for swimming upstream as it employs an algorithm that can estimate the effect of biases and the expression level of genes using experimental data.
Kingsford said this is important because this technique is increasingly used for classifying diseases and their subtypes, understanding gene expression changes during development, and tracking the progression of disease.
Read the full press release
here.
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