Motivation: Differential gene expression (DGE) is a ubiquitous but ill-defined analysis. After transcription is initiated, most mRNAs are spliced immediately in a regulated manner yielding a defined mixture of different transcripts of the gene. Thus, a DGE analysis should also include an analysis of the changes in the transcript composition provided by differential alternative splicing (DAS) analysis.

Results: EmpiReS is an approach for model-free quantification of directed feature fold changes via Empirical error distributions estimated from Replicate Sequencing measurements. We comprehensively assess the performance of EmpiReS for DGE and the more complex doubly differential DAS. For both analyses, we could show that EmpiReS is highly reliable and has an excellent trade-off between sensitivity and precision. It outperforms state-of-the-art methods such as DEXSeq and is orders of magnitude faster which allows it to jointly analyse DGE and DAS which provides a more realistic view of the changes in the cell.

Availability and Implementation: the EmpiReS software is available at https://github.com/zimmerlab/EmpiReS

Co-authors: Gergely Csaba, Armin Hadziahmetovic, Markus Gruber, Constantin Ammar, Ralf Zimmer