KEGGanim

KEGGanim visualizes high-throughput experimental data by overlaying measurements onto KEGG pathway maps to facilitate interpretation of gene and protein expression changes across conditions.


Key Features:

  • Visualization capabilities: Generates animations and static images that overlay experimental measurements onto hand-drawn KEGG pathway maps to show condition-dependent changes.
  • Data integration: Accepts public or user-uploaded high-throughput datasets, including microarrays, and supports common gene and protein identifiers used in proteomics and interactomics.
  • Dynamic highlighting: Colors pathway members according to experimental measurements to highlight dynamic changes across different conditions and aid identification of important modules and genes.
  • Broad applicability: Supports pathway visualization for 14 organisms and leverages extensive public microarray data for those species.

Scientific Applications:

  • Gene expression analysis: Provides pathway-context visualization of microarray and other expression data to aid interpretation of differential expression.
  • Multi-omics integration: Accommodates proteomics and interactomics identifiers to combine diverse -omics datasets on KEGG maps.
  • Pathway dynamics and module identification: Uses color-coded and animated visualizations to reveal dynamic pathway behavior and potential key regulatory elements across conditions.

Methodology:

Maps experimental measurements from public or user-uploaded high-throughput datasets (e.g., microarrays) onto KEGG pathway maps with color-coding of gene or protein expression and generates static images and animations to visualize changes across conditions.

Topics

Collections

Details

License:
Freeware
Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Perl
Added:
8/24/2015
Last Updated:
1/19/2020

Operations

Data Inputs & Outputs

Filtering

Publications

Adler P, Reimand J, Jänes J, Kolde R, Peterson H, Vilo J. KEGGanim: pathway animations for high-throughput data. Bioinformatics. 2007;24(4):588-590. doi:10.1093/bioinformatics/btm581.

Documentation