Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for analysing alternative splicing integrating eleven splice-aware mapping and eight event detection tools. Researchers at the Technical University of Munich benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, the researchers propose the first reporting standard to unify existing formats and to guide future tool development.
1) simulated (ASimulatoR) or user-provided fastq files 2) bam files could be generated by any of 11 supported splice-aware mapping tools; 3) AS events detected by any of 8 AS event detection tools based on files generated in the previous steps; 4) the output files of the AS detection tools are unified by DICAST. Created with BioRender.com.
Availability – The DICAST source code and documentation are available at https://github.com/CGAT-Group/DICAST and https://dicast.readthedocs.io/en/master/contents.html, respectively.