Inferring metabolic pathway activity levels from RNA-Seq data

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.

Researchers at Georgia State University have developed 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 analysis flow


The branches represent the two approaches used to compute pathway significance in the case of graph-based on the left and pathway activity level in the case of the expectation maximization approach on the right. Both methods are validated by computing contigs/transcripts differential expressions and qPCR as the last step of the flow

The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms.

Availability – The source code and installation instructions are available at

Temate-Tiagueu Y, Seesi SA, Mathew M, Mandric I, Rodriguez A, Bean K, Cheng Q, Glebova O, Măndoiu I, Lopanik NB, Zelikovsky A. (2016) Inferring metabolic pathway activity levels from RNA-Seq data. BMC Genomics 17 Suppl 5:542. [article]

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