Systematic analysis of alternative splicing in time course data using Spycone

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 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/. Supplementary information Supplementary data are available at Bioinformatics online.

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

“…The Spycone method predominantly highlighted switching transcripts with high expression levels by calculating the event importance metrics. While TSIS found a lot of low expressed transcripts based on previous findings [37]. Furthermore, while Spycone focuses on detecting IS events, our simulation study revealed that both LimmaDS and DEXSeq do not differentiate between DTU scenarios.…”

“…There are two time series tools for detection of isoform switches in time series data: TSIS [36] and Sypcone [37]. TSIS and Spycone are the tools that detect switch points between transcript pairs and calculate adjusted p-values based on the replicates.…”

“…Time series tools TSIS R 2017 [36] detection and characterization of isoform switches for time series data Spycone python 2023 [37] detection and characterization of isoform switches for time series data…”

“…The time series RNA-seq data of SARS-Cov-2 infection data was obtained from GSE157490 [19]. The data is processed as described in [40].…”

ASTK: A Machine Learning‐Based Integrative Software for Alternative Splicing Analysis

“…[1] While AS is critical for normal physiological functions, aberrations in this process can lead to a spectrum of diseases. [2] Numerous software tools have been crafted to aid AS research, ranging from the detection and quantification of AS events [3][4][5][6][7][8][9] to time series and isoform switch analysis, [10,11] functional enrichment, [12,13] mapping the spatial distribution of RNA-binding proteins (RBPs) binding motifs, [14] sequence feature extractions, [15,16] and assessing the functional implications of AS changes. [16] Historically, AS studies have been focusing on two main aspects: functionality and regulation.…”