Alternative splicing (AS) is emerging as an important regulatory process for complex biological processes. Transcriptomic studies therefore commonly involve the identification and quantification of alternative processing events, but the need for predicting the functional consequences of changes to the relative inclusion of alternative events remains largely unaddressed. Many tools exist for the former task, albeit each constrained to its own event type definitions. Few tools exist for the latter task; each with significant limitations. To address these issues we developed junctionCounts, which captures both simple and complex pairwise AS events and quantifies them with straightforward exon-exon and exon-intron junction reads in RNA-seq data, performing competitively among similar tools in terms of sensitivity, false discovery rate and quantification accuracy. Its partner utility, cdsInsertion, identifies transcript coding sequence (CDS) information via
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Ritter et al. (2024) studied this question.
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