Systematic analysis of alternative splicing in time course data using Spycone

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.

Network-based approaches for modeling disease regulation and progression

“…Links to code/tools are provided in Supplementary Table 1 . Tool Input data type (s) Algorithm Type Example application Reference SigMod GWAS Aggregate score Identify functionally and biologically relevant genes in childhood-onset asthma [18] [18] IODNE Gene expression Aggregate score Identify potentially novel target genes for drug selection in triple-negative breast cancer [19] [19] PCSF Multi-omics data (gene expression, mutation profiles, or copy number) Aggregate score Extract subnetworks of enriched metabolite interactions in multiple sclerosis [36] [20] Omics Integrator Gene expression Aggregate score Link α-synuclein to multiple parkinsonism genes and druggable targets [37] [21] MuST Disease-associated genes (derived from GWAS or DEG analyses) Aggregate score Investigation of coagulation pathway in COVID-19 [38] [22] ROBUST Disease-associated genes (derived from GWAS or DEG analyses) Aggregate score Identify an oxidative stress module in multiple sclerosis [23] [23] DOMINO Disease-associated genes (derived from GWAS or DEG analyses) Aggregate score Integrated as the downstream analysis step in a splicing-aware framework for time course data analysis [39] [24] KeyPathwayMiner Gene expression / multi-omics data Module cover Reveal epigenetic targets in SARS-CoV-2 infection, used together with gene co-expression networks [40] [25] ModuleDiscoverer Gene expression Module cover Identify regulatory modules o...…”