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.
PMID: 14764551