ChromoViz

ChromoViz visualizes multimodal genomic datasets, including microarray gene expression data, DNA copy number alterations, and non-expression genomic information, to support cross-species and cross-platform comparative genomic analyses.


Key Features:

  • Multimodal Visualization: Supports visualization of microarray gene expression profiles, DNA copy number alterations, and non-expression genomic information together for integrated analysis.
  • Cross-Species and Cross-Platform Comparisons: Enables comparative analyses across species and experimental platforms to identify conserved genetic elements and platform-specific biases.
  • Integration with Public Databases: Incorporates non-expression genomic data sourced from public databases to combine expression data with additional genomic annotations.
  • Chromosomal Visualization Format: Implements a chromosomal visualization format that decouples the data layer from the procedure layer for flexible representation of datasets on chromosomes.

Scientific Applications:

  • Evolutionary Biology: Facilitates analysis of conserved genomic elements and divergence across species using integrated genomic data.
  • Comparative Genomics: Supports identification of conserved genetic elements and platform-specific biases through cross-species and cross-platform comparisons.
  • Multi-omic Integration: Enables joint visualization and comparison of expression, copy number, and other genomic annotations for multi-omic analyses.

Methodology:

Implemented as an R package that separates data processing from visualization and implements a chromosomal visualization format that decouples the data layer from the procedure layer.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
12/18/2017
Last Updated:
11/25/2024

Operations

Publications

Kim J, Chung H, Park CH, Park W, Kim JH. ChromoViz: multimodal visualization of gene expression data onto chromosomes using scalable vector graphics. Bioinformatics. 2004;20(7):1191-1192. doi:10.1093/bioinformatics/bth052. PMID:14764551.

Links