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. Availability and implementation: The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/.
Chit Tong Lio
PhD student
Zakaria Louadi
Researcher at Illumina Artificial Intelligence Laboratory
Amit Fenn
Postdoctoral Researcher at Helmholtz Munich
Olga Tsoy
Group Leader
I’m a group leader of Computational Genomics and Transcriptomics group. I focus on the BMBF funded Sys_CARE project about alternative splicing in cardiac and renal diseases. I graduated from Lomonosov Moscow State University and did PhD in Kharkevich Institute in Moscow at professor Gelfand lab. There I studied the evolution of transcriptional regulatory networks in bacteria. During my PhD I shortly stayed at Stowers Institute for Medical Research, Kansas-City, where I went on working on regulation of bacterial metabolic pathways. As a research scientist, I worked in Skolkovo Institute of Science and Technology, Moscow on 3D chromatin structure analysis.