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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Pathway collages: personalized multi-pathway diagrams.

Suzanne Paley1, Paul E O'Maille2,3, Daniel Weaver1

  • 1Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, 94025, USA.

BMC Bioinformatics
|December 15, 2016
PubMed
Summary
This summary is machine-generated.

Scientists can now create custom metabolic pathway diagrams called pathway-collages. This software helps visualize complex interactions for better biochemical analysis.

Keywords:
Metabolic pathwaysPathway diagramVisualization

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Bioinformatics

Background:

  • Metabolic pathway diagrams are essential for visualizing biochemical reactions.
  • Analyzing complex biological systems requires viewing multiple interacting pathways, but full metabolic networks can be overwhelming.
  • There is a need for tools to construct personalized, multi-pathway diagrams that highlight specific interactions.

Purpose of the Study:

  • To develop a method for creating personalized multi-pathway diagrams, termed pathway-collages.
  • To enable scientists to visualize selected interacting pathways and their connections efficiently.

Main Methods:

  • Developed pathway-collage software combining manual and automatic layout generation.
  • Users select pathways of interest from a Pathway/Genome Database.
  • Pathway layouts generated by Pathway Tools are integrated into a Javascript application (Cytoscape.js) for user customization.

Main Results:

  • The pathway-collage application allows users to reposition pathways and define inter-pathway connections.
  • Users can modify visual styles and overlay multi-omics data (metabolomics, gene expression, reaction flux).
  • Demonstrated utility in a metabolomics study of pathogen drug response and an E. coli metabolic model.

Conclusions:

  • Pathway collages provide a user-friendly approach to constructing personalized multi-pathway visualizations.
  • This method facilitates the analysis of complex biochemical scenarios by focusing on relevant pathway interactions.