TSSpredator-Web: A web-application for transcription start site prediction and exploration

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• High-throughput RNA sequencing (RNA-seq) enables detailed prokaryotic transcriptome analysis.
• Transcription start site (TSS) identification is crucial for understanding transcriptional regulation.
• Existing command-line tools for TSS prediction lack user-friendliness and integrated exploration features.

Purpose of the Study:

• To introduce TSSpredator-Web, an interactive web application enhancing the usability of the TSSpredator tool.
• To facilitate TSS prediction from various RNA-seq data types.
• To provide interactive visualization and exploration of TSS prediction results.

Main Methods:

• Development of TSSpredator-Web, a web application based on the TSSpredator tool.
• Implementation of TSS prediction for non-enriched and enriched RNA-seq data.
• Integration of dynamic visualizations (UpSet plot, genome viewer) and interactive tables for result exploration.

Main Results:

• TSSpredator-Web enables user-friendly TSS prediction and classification relative to annotated genes.
• Interactive visualizations, including an UpSet plot and genome viewer, contextualize TSS predictions.
• A use case with Escherichia coli Cappable-seq data demonstrates the tool's utility.

Conclusions:

• TSSpredator-Web simplifies genome-wide TSS analysis and interpretation in prokaryotes.
• The platform empowers a wider range of researchers to derive biological insights from transcriptomic data.
• Enhanced accessibility and interactive exploration significantly facilitate prokaryotic gene regulation studies.