Inferring metabolic pathway activity levels from RNA-Seq data

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Assessing metabolic pathway activity is crucial for understanding biological differences.
• Traditional methods rely on microarray data, but next-generation sequencing (NGS) demands new bioinformatics tools.
• RNA-Seq data analysis for pathway activity is increasingly important.

Purpose of the Study:

• Introduce XPathway, a novel bioinformatics toolset for analyzing pathway activity directly from RNA-Seq data.
• Compare metabolic pathway activity between different biological conditions using RNA-Seq.
• Provide a method for quantifying metabolic differences.

Main Methods:

• Developed XPathway tools for analyzing RNA-Seq data.
• Mapped assembled contigs from RNA-Seq reads to KEGG pathways.
• Employed expectation maximization and pathway graph topology for activity analysis.

Main Results:

• Applied XPathway to RNA-Seq data from Bugula neritina with and without its symbiont.
• Successfully identified several metabolic pathways with differential activity levels.
• Validated enzyme expression from identified pathways using quantitative PCR (qPCR).

Conclusions:

• XPathway effectively detects and quantifies metabolic differences between two samples.
• The software is implemented in C, Python, and shell scripting for Linux/Unix platforms.
• Source code and installation instructions are publicly available.