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

rna-seq

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 http://alan.cs.gsu.edu/NGS/?q=content/xpathway

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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