Systematic analysis of alternative splicing in time course data using Spycone

During disease progression or organism development, alternative splicing (AS) may lead to isoform switches (IS) that demonstrate similar temporal patterns and reflect the AS co-regulation of such genes. Tools for dynamic process analysis usually neglect AS. Here we propose Spycone (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/yollct/spycone">https://github.com/yollct/spycone</ext-link>), a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.