title: Inferring metabolic pathway activity levels from RNA-Seq data.
abstract: 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 .
authors: Department of Computer Science, Georgia State University, 34 Peachtree St., Atlanta, 30303, GA, USA. ytematetiagueu1@cs.gsu.edu.
authors: Yvette
authors: Y
authors: Temate-Tiagueu
authors: Computer Science & Engineering Department, University of Connecticut, Storrs, 06269, CT, USA.
authors: Sahar Al
authors: SA
authors: Seesi
authors: Department of Biology, Georgia State University, 100 Piedmont Ave., Atlanta, 30303, GA, USA.
authors: Meril
authors: M
authors: Mathew
authors: Department of Computer Science, Georgia State University, 34 Peachtree St., Atlanta, 30303, GA, USA.
authors: Igor
authors: I
authors: Mandric
authors: Department of Biology, Georgia State University, 100 Piedmont Ave., Atlanta, 30303, GA, USA.
authors: Alex
authors: A
authors: Rodriguez
authors: Department of Biology, Georgia State University, 100 Piedmont Ave., Atlanta, 30303, GA, USA.
authors: Kayla
authors: K
authors: Bean
authors: Department of Pharmacology, University of Miami, Miami, FL, USA.
authors: Qiong
authors: Q
authors: Cheng
authors: Department of Computer Science, Georgia State University, 34 Peachtree St., Atlanta, 30303, GA, USA.
authors: Olga
authors: O
authors: Glebova
authors: Computer Science & Engineering Department, University of Connecticut, Storrs, 06269, CT, USA. ion@engr.uconn.edu.
authors: Ion
authors: I
authors: Măndoiu
authors: Department of Biology, Georgia State University, 100 Piedmont Ave., Atlanta, 30303, GA, USA. nicole.lopanik@eas.gatech.edu.|Current address: School of Earth and Atmospheric Sciences, School of Biological Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, 30332, GA, USA. nicole.lopanik@eas.gatech.edu.
authors: Nicole B
authors: NB
authors: Lopanik
authors: Department of Computer Science, Georgia State University, 34 Peachtree St., Atlanta, 30303, GA, USA. alexz@cs.gsu.edu.
authors: Alexander
authors: A
authors: Zelikovsky
mesh_terms: D000818:Animals; D002013:Bryozoa / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology; D053858:Metabolic Networks and Pathways; D017423:Sequence Analysis, RNA; D012984:Software; D013559:Symbiosis; D059467:Transcriptome