Systematic analysis of alternative splicing in time course data using Spycone

Spycone is available as a python package that provides systematic analysis of time course transcriptomics data. Figure 1 shows the workflow of Spycone. It uses gene or isoform expression and a biological network as an input. It employs the sum of changes of all isoforms relative abundance (total isoform usage) across time points to detect IS events. It further provides downstream analysis such as clustering by total isoform usage, gene set enrichment analysis, network enrichment, and splicing factors analysis.

The SARS-Cov-2 infection and cancer dataset are used as an application demonstration for our Spycone tool and a simulation dataset is used for benchmark analysis. 

The rhinovirus dataset and SARS-Cov-2 infection (3 time points) for the tutorial in the documentation are included here. 

The simulated dataset from the 2 models described in the manuscript are uploaded as zen_simdata_{model}_{noise}.csv.

A gtf file used in the splicing factor analysis, both in the manuscript and tutorial. Derived from ensembl GRCh38.99.