TraV: a genome context sensitive transcriptome browser.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) technologies generate vast amounts of genomic data, particularly for transcriptional analysis.
• Existing RNA-Seq analysis and visualization tools struggle with large datasets, often limited to processing only two to three samples simultaneously due to memory and processing constraints.

Purpose of the Study:

• To develop an efficient RNA-Seq analysis and visualization tool capable of processing multiple transcriptome sequencing experiments.
• To address the limitations of current tools in handling large-scale prokaryotic RNA-seq data.

Main Methods:

• Development of TraV, a novel RNA-Seq analysis and visualization tool.
• Implementation of analytical methods specifically for prokaryotic RNA-seq experiments.
• Calculation of single nucleotide activities from mapping information for memory-efficient analysis.

Main Results:

• TraV successfully processed fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13.
• The tool utilizes nucleotide activities, significantly improving memory efficiency and reducing processing overhead compared to single read mapping.
• TraV enables the visualization and analysis of multiple transcriptome sequencing experiments simultaneously.

Conclusions:

• TraV offers a memory-efficient and effective solution for analyzing and visualizing multiple prokaryotic RNA-seq experiments.
• The nucleotide activity approach overcomes the limitations of existing tools, facilitating deeper insights into transcriptional activities from large datasets.