Inferring metabolic pathway activity levels from RNA-Seq data

Yvette Temate-Tiagueu, Sahar Al Seesi, Meril Mathew, Igor Mandric, Alex Rodriguez, Kayla Bean, Qiong Cheng, Olga Glebova, Ion Măndoiu, Nicole B. Lopanik, Alexander Zelikovsky

Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs. XPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium "Candidatus Endobugula sertula". We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR). Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway .