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Inferring metabolic pathway activity levels from RNA-Seq data
- Source :
- BMC Genomics
- Publication Year :
- 2016
- Publisher :
- BioMed Central, 2016.
-
Abstract
- 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. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2823-y) contains supplementary material, which is available to authorized users.
- Subjects :
- 0301 basic medicine
RNA-Seq
Biology
Proteomics
Bryozoa
Transcriptome
03 medical and health sciences
Genetics
Animals
natural sciences
KEGG
Symbiosis
computer.programming_language
Sequence Analysis, RNA
Shell script
Research
Computational Biology
Python (programming language)
Metabolic pathway
030104 developmental biology
DNA microarray
computer
Metabolic Networks and Pathways
Software
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 17
- Issue :
- Suppl 5
- Database :
- OpenAIRE
- Journal :
- BMC Genomics
- Accession number :
- edsair.doi.dedup.....22e88d216a1e36e2f8d0e61348860ae3