In the online edition of Nucleic Acids Research, investigators from the US and Korea outlined a computational strategy for dialing down off-target interactions identified when doing loss-of-function screens with small interfering RNAs. The "Deconvolution Analysis of RNAi screening data," or DecoRNAi, method can automatically assess and diminish rates of off-target effects, according to the study's authors, who validated the strategy using data from five siRNA screens. "DecoRNAi is a computational tool that fills an important unmet need for the functional genomics research community," they write, "as it enhances the return of rigorous biologically meaningful observations."
Bringing together whole-exome sequence and RNA sequence data can uncover somatic mutations in tumor samples that would be missed by applying the DNA sequencing method alone, according to another NAR study. There, a University of North Carolina at Chapel Hill-led team describes its so-called "UNCeqR" approach for finding such mutations with integrated DNA and RNA sequence data. Based on analyses of data for hundreds of simulated and real tumors, the researchers determined that UNCeqR is particularly adept at detecting mutations in low purity tumors that are missed by other methods.
Vertebrate genomics researchers from the Max Planck Institute for Molecular Genetics present a tool for finding differential splicing events in RNA sequencing data. The method, known as ARH-seq, builds on a previously presented approach called ARH that was used to see differential splicing markers from exon microarray information. In proof-of-principle experiments using large, publicly available human sequence datasets, the team demonstrated that ARH-seq performed favorably for picking up differential splicing events when compared with eight other computational methods.