Speakers
Description
Introduction: Alternative splicing (AS) drives protein and transcript diversity and is known to play a role in many diseases. The exact mechanisms controlling the AS machinery are currently insufficiently understood. During disease progression or organism development, AS may lead to isoform switches (IS) that follow temporal patterns. Several IS genes occurring at the same time point could reflect the co-regulation of AS for such genes.
The only published method for time-course isoform analysis, TSIS (Guo et al. 2017) provides a list of IS but does not investigate if these co-occur. This emphasizes the need for new methods for the systematic detection of IS patterns.
Method: We propose Spycone, a splicing-aware systematic framework for time-course data analysis. Spycone clusters genes and isoforms with similar temporal expression patterns. For isoform level analysis, we developed a novel IS detection algorithm that studies changes in total isoform abundance across time-course. Spycone couples the time-course clustering analysis with downstream analysis such as network enrichment and gene set enrichment analysis for functional interpretation. To evaluate the performance of Spycone, we implemented a novel approach for simulating time-course data.
Results: We demonstrate the performance of Spycone and TSIS using simulated and real-world RNA-seq data of SARS-Cov2 infection development (Kim et al. 2021). On the simulated data set, Spycone outperforms its closest competitor TSIS in terms of precision and recall. On the real-world data set, Spycone identified gene network modules involved in cell response after SARS-Cov2 infection, uniquely highlighting changes in AS associated with the disease.
Conclusion: Spycone identifies genes with co-occurring IS in time-course RNA-seq data and allows for their functional interpretation through network enrichment analysis. Spycone, thus, offers a unique systems medicine view on the temporal cellular regulation of AS.
References:
Guo, Wenbin, Cristiane P. G. Calixto, John W. S. Brown, and Runxuan Zhang. 2017. “TSIS: An R Package to Infer Alternative Splicing Isoform Switches for Time-Series Data.” Bioinformatics 33 (20): 3308–10.
Kim, Doyeon, Sukjun Kim, Joori Park, Hee Ryung Chang, Jeeyoon Chang, Junhak Ahn, Heedo Park, et al. 2021. “A High-Resolution Temporal Atlas of the SARS-CoV-2 Translatome and Transcriptome.” Nature Communications 12 (1): 5120.
