Inferring metabolic pathway activity levels from RNA-Seq data

Background 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 act...

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Published in:BMC genomics Vol. 17; no. Suppl 5; p. 542
Main Authors: Temate-Tiagueu, Yvette, Seesi, Sahar Al, Mathew, Meril, Mandric, Igor, Rodriguez, Alex, Bean, Kayla, Cheng, Qiong, Glebova, Olga, Măndoiu, Ion, Lopanik, Nicole B., Zelikovsky, Alexander
Format: Journal Article
Language:English
Published: London BioMed Central 31.08.2016
BioMed Central Ltd
Subjects:
Animal Genetics and Genomics
Animals
Biomedical and Life Sciences
Bryozoa - genetics
Bryozoa - metabolism
Computational Biology
Gene expression
Life Sciences
Metabolic Networks and Pathways
Microarrays
Microbial Genetics and Genomics
Observations
Plant Genetics and Genomics
Proteomics
RNA sequencing
Sequence Analysis, RNA
Software
Symbiosis
Transcriptome
Differential Expression Analysis
Permutation Model
Pathway Activity
Pathway Graph
Ortholog Group
ISSN:1471-2164, 1471-2164
Online Access:Get full text
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Summary:Background 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. Results 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). Conclusions 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 .
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-016-2823-y