Exploring transcriptional switches from pairwise, temporal and population RNA-Seq data using deepTS

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Transcriptional switch (TS) involves altered relative transcript expression from the same gene.
• TS is linked to human diseases, plant development, and stress responses.
• Existing tools lack comprehensive and flexible TS analysis for RNA sequencing (RNA-Seq) data.

Purpose of the Study:

• To present deepTS, a user-friendly web-based tool for TS event analysis.
• To enable interactive, multifunctional identification, visualization, and analysis of TS events.
• To facilitate large-scale RNA-Seq data analysis for TS events.

Main Methods:

• Development of a web-based implementation named deepTS.
• Integration of interactive and multifunctional TS identification and visualization.
• Support for pairwise, temporal, and population RNA-Seq experiments.

Main Results:

• deepTS provides streamlined RNA-Seq-based TS analysis.
• The tool supports model and non-model organisms, with or without reference transcriptomes.
• Case studies demonstrate deepTS's capability for transcriptome-wide TS analysis.

Conclusions:

• deepTS enhances accessibility and reproducibility of TS analyses for large-scale RNA-Seq data.
• The tool empowers research groups with varying informatics expertise.
• deepTS facilitates collaborative and efficient TS event investigation.