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Related Experiment Video

Updated: Mar 15, 2026

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture
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Inferring metabolic pathway activity levels from RNA-Seq data.

Yvette Temate-Tiagueu1, Sahar Al Seesi2, Meril Mathew3

  • 1Department of Computer Science, Georgia State University, 34 Peachtree St., Atlanta, 30303, GA, USA. ytematetiagueu1@cs.gsu.edu.

BMC Genomics
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

XPathway analyzes RNA-Seq data to quantify metabolic pathway activity, identifying differences between samples. This bioinformatics tool aids in understanding biological variations using next-generation sequencing (NGS) data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Assessing metabolic pathway activity is crucial for understanding biological differences.
  • Traditional methods rely on microarray data, but next-generation sequencing (NGS) demands new bioinformatics tools.
  • RNA-Seq data analysis for pathway activity is increasingly important.

Purpose of the Study:

  • Introduce XPathway, a novel bioinformatics toolset for analyzing pathway activity directly from RNA-Seq data.
  • Compare metabolic pathway activity between different biological conditions using RNA-Seq.
  • Provide a method for quantifying metabolic differences.

Main Methods:

  • Developed XPathway tools for analyzing RNA-Seq data.
  • Mapped assembled contigs from RNA-Seq reads to KEGG pathways.
  • Employed expectation maximization and pathway graph topology for activity analysis.

Main Results:

  • Applied XPathway to RNA-Seq data from Bugula neritina with and without its symbiont.
  • Successfully identified several metabolic pathways with differential activity levels.
  • Validated enzyme expression from identified pathways using quantitative PCR (qPCR).

Conclusions:

  • XPathway effectively detects and quantifies metabolic differences between two samples.
  • The software is implemented in C, Python, and shell scripting for Linux/Unix platforms.
  • Source code and installation instructions are publicly available.