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. 2023 May 30;5(2):lqad044.
doi: 10.1093/nargab/lqad044. eCollection 2023 Jun.

Alternative splicing analysis benchmark with DICAST

Amit Fenn  1   2 , Olga Tsoy  2 , Tim Faro  1 , Fanny L M Rößler  1 , Alexander Dietrich  1 , Johannes Kersting  1 , Zakaria Louadi  1   2 , Chit Tong Lio  1   2 , Uwe Völker  3   4 , Jan Baumbach  2   5 , Tim Kacprowski  6   7 , Markus List  1
Affiliations

Alternative splicing analysis benchmark with DICAST

Amit Fenn et al. NAR Genom Bioinform. .

Abstract

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. We 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, we propose the first reporting standard to unify existing formats and to guide future tool development.

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Figures

Figure 1.
Figure 1.
DICAST framework: 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.
Figure 2.
Figure 2.
(A) The plots for precision and fraction of unmapped reads plots for splice-aware mapping tools for the data sets S0 and S5. The results for S1–S4 are presented in Supplementary Figure S3. The values are taken from the analysis of one out of five simulated datasets as an example. (B) The boxplots for precision and fraction of unmapped reads for splicing-aware mapping tools calculated for S0 (repeated 5 times) and S5 (repeated 5 times)
Figure 3.
Figure 3.
(A) Precision/recall plots for AS event detection tools for data sets S1 and S5. The values are taken from the analysis of one out of five simulated datasets as an example. (B) Tool performance on S5 (bigger markers) and S5-tr (smaller markers) sets by AS event type for the sequencing depth of 50M reads.(C) Precision/recall boxplots for AS event detection tools for the S0 (repeated 5 times) and S5 (repeated 5 times)
Figure 4.
Figure 4.
The UpSet plot shows the intersection of results from evaluated AS detection tools.
Figure 5.
Figure 5.
The run time in minutes of the splice-aware mapping tools and AS event detection tools.
See this image and copyright information in PMC

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