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bioRxiv · 10.1101/2022.04.28.489857

Systematic analysis of alternative splicing in time course data using Spycone

Lio, C. T.·Louadi, Z.·Fenn, A.·Baumbach, J.·Kacprowski, T.·List, M.·Tsoy, O.
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Abstract

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 (https://github.com/yollct/spycone), 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.

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Lio, C. T., Louadi, Z., Fenn, A., Baumbach, J., Kacprowski, T., List, M., Tsoy, O.. 2022-04-29. Systematic analysis of alternative splicing in time course data using Spycone. https://doi.org/10.1101/2022.04.28.489857

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