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
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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 .
AbstractList	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.BACKGROUNDAssessing 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).RESULTSXPathway 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 .CONCLUSIONSOur 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 . 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 . 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 .
ArticleNumber	542
Audience	Academic
Author	Rodriguez, Alex Mandric, Igor Glebova, Olga Seesi, Sahar Al Cheng, Qiong Lopanik, Nicole B. Măndoiu, Ion Mathew, Meril Temate-Tiagueu, Yvette Zelikovsky, Alexander Bean, Kayla
Author_xml	– sequence: 1 givenname: Yvette surname: Temate-Tiagueu fullname: Temate-Tiagueu, Yvette email: ytematetiagueu1@cs.gsu.edu organization: Department of Computer Science, Georgia State University – sequence: 2 givenname: Sahar Al surname: Seesi fullname: Seesi, Sahar Al organization: Computer Science & Engineering Department, University of Connecticut – sequence: 3 givenname: Meril surname: Mathew fullname: Mathew, Meril organization: Department of Biology, Georgia State University – sequence: 4 givenname: Igor surname: Mandric fullname: Mandric, Igor organization: Department of Computer Science, Georgia State University – sequence: 5 givenname: Alex surname: Rodriguez fullname: Rodriguez, Alex organization: Department of Biology, Georgia State University – sequence: 6 givenname: Kayla surname: Bean fullname: Bean, Kayla organization: Department of Biology, Georgia State University – sequence: 7 givenname: Qiong surname: Cheng fullname: Cheng, Qiong organization: Department of Pharmacology, University of Miami – sequence: 8 givenname: Olga surname: Glebova fullname: Glebova, Olga organization: Department of Computer Science, Georgia State University – sequence: 9 givenname: Ion surname: Măndoiu fullname: Măndoiu, Ion email: ion@engr.uconn.edu organization: Computer Science & Engineering Department, University of Connecticut – sequence: 10 givenname: Nicole B. surname: Lopanik fullname: Lopanik, Nicole B. email: nicole.lopanik@eas.gatech.edu organization: Department of Biology, Georgia State University, Current address: School of Earth and Atmospheric Sciences, School of Biological Sciences, Georgia Institute of Technology – sequence: 11 givenname: Alexander surname: Zelikovsky fullname: Zelikovsky, Alexander email: alexz@cs.gsu.edu organization: Department of Computer Science, Georgia State University
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Keywords	Differential Expression Analysis Permutation Model Pathway Activity Pathway Graph Ortholog Group
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Snippet	Background Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from... Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray...
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SubjectTerms	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
Title	Inferring metabolic pathway activity levels from RNA-Seq data
URI	https://link.springer.com/article/10.1186/s12864-016-2823-y https://www.ncbi.nlm.nih.gov/pubmed/27585456 https://www.proquest.com/docview/1816630800 https://pubmed.ncbi.nlm.nih.gov/PMC5009525
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