Robust data-driven gene expression inference for RNA-seq using curated intergenic regions

RNA-Seq is a powerful technique to provide quantitative information on gene expression. While many applications focus on measuring expression levels, accurately distinguishing between actively and inactively transcribed genes is equally important for understanding gene function, development, and disease mechanisms. However, setting a biologically meaningful threshold for calling genes expressed is challenging due to variability in noise levels across different protocols, experiments or biological samples. We propose to define this threshold per sample relative to the background level observed in inactive genomic features, inferred by the amount of reads mapped to intergenic regions of the genome, and to call genes expressed if their level of expression is significantly higher than the estimated background noise. This approach can be applied to a single RNA-Seq library as well as to a combination of libraries from the same condition, in model and non-model organisms. We show that our method yields a more accurate prediction of expression state than existing methods, illustrated by consistent expression calls for biological replicates in the same tissue.