Article,
Systematic analysis of alternative splicing in time course data using Spycone
Contributors
Lio C.T.
0000-0003-2297-831X
[1]
[2]
Fenn A.
0000-0003-2203-3922
[1]
[2]
Baumbach J.
0000-0002-0282-0462
[1]
[5]
Kacprowski T.
0000-0002-5393-2413
[3]
[4]
List M.
0000-0002-0941-4168
[2]
Tsoy O.
0000-0002-7592-2080
(Corresponding author)
[1]
Affiliations
- [1] Universität Hamburg [NORA names: Germany; Europe, EU; OECD];
- [2] Technische Universität München [NORA names: Germany; Europe, EU; OECD];
- [3] Division Data Science in Biomedicine [NORA names: Germany; Europe, EU; OECD];
- [4] Technische Universität Braunschweig [NORA names: Germany; Europe, EU; OECD];
- [5] University of Southern Denmark [NORA names: SDU University of Southern Denmark; University; Denmark; Europe, EU; Nordic; OECD]
Abstract
Motivation: During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing. Results: Here, we propose 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.
