Computational Comparison of Differential Splicing Tools for Targeted RNA Long-Amplicon Sequencing (rLAS)

TL;DR

This paper compares four computational tools for analyzing splicing events in RNA long-amplicon sequencing data, finding that MAJIQ performs best overall while rMATS excels at detecting exon-skipping events.

Contribution

The study provides the first evaluation of splicing tool performance specifically for targeted RNA long-amplicon sequencing (rLAS) data.

Findings

MAJIQ detected all types of splicing events except one exon-skipping event.

rMATS detected all exon-skipping events but failed to detect other splicing event types.

MISO and SplAdder failed to detect any of the tested splicing events.

Abstract

RNA sequencing (RNA-Seq) is a powerful technique for the quantification of transcripts and the analysis of alternative splicing. Previously, our laboratory developed the targeted RNA long-amplicon sequencing (rLAS) method, which has the advantage of allowing deep analysis of targeted specific transcripts. The computational tools for analyzing RNA-Seq data have boosted alternative splicing research by detecting and quantifying splicing events. However, the performance of these splicing tools has not yet been investigated for rLAS. Here, we evaluated the performance of four splicing tools (MAJIQ, rMATS, MISO, and SplAdder) using samples with different types of known splicing events (exon-skipping, multiple-exon-skipping, alternative 5′ splicing, and alternative 3′ splicing). MAJIQ was able to detect all of the types of events, but it was unable to detect one of the exon-skipping events. On the other hand, rMATS was able to detect all of the exon-skipping events. However, rMATS failed to detect other types of events besides exon-skipping events. Both MISO and SplAdder were unable to detect any of the events. These results indicate that MAJIQ presents better performance for the different types of splicing events in rLAS and that rMATS shows better performance for exon-skipping splicing events.

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