RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex mo-lecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. One of the many advantages of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage still remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed metaDIEA (meta- Differential Isoform Expression Analysis), a novel integrative ap-proach that effectively combines the top most widely used algorithms for differential transcript and isoform analysis using state-of-the-art Machine Learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as Docker containers.
This project is licensed under the MIT License.