Genoscapist
Genoscapist generates high-quality graphical representations of quantitative genomic profiles to visualize and analyze hundreds of profiles along a reference genome and integrate genome annotations for transcriptome and gene expression studies.
Key Features:
- Genome-scale visualization: Produces graphical representations of quantitative profiles mapped along a reference genome.
- Multi-profile handling: Simultaneously displays and compares hundreds of quantitative genomic profiles.
- Annotation integration: Integrates various genome annotations with quantitative data within the visual outputs.
- Customizable image generation: Generates high-quality, customizable images for detailed examination of genomic regions.
- Transcriptome-scale support: Supports analysis of large-scale transcriptome datasets and condition-dependent variations.
- Organism deployments: Has been applied to transcriptome datasets from Bacillus subtilis and Staphylococcus aureus.
Scientific Applications:
- Transcriptome analysis: Comparative and condition-dependent analysis of transcriptome datasets across genomes.
- Gene expression and regulation: Examination of gene expression patterns and regulatory features from quantitative genomic profiles.
- Large-scale genomic studies: Visualization and integrative analysis of large-scale genomic datasets to support hypothesis generation.
- Organism-specific investigations: Analysis of transcriptomic data for Bacillus subtilis and Staphylococcus aureus.
Methodology:
Generates high-quality images by mapping quantitative genomic profiles onto a reference genome and integrating genome annotations, supporting simultaneous rendering of hundreds of profiles and customizable visual outputs.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- command-line tool
- Programming Languages:
- JavaScript, Python
- Added:
- 3/19/2021
- Last Updated:
- 3/26/2021
Operations
Publications
Dérozier S, Nicolas P, Mäder U, Guérin C. Genoscapist: online exploration of quantitative profiles along genomes via interactively customized graphical representations. Bioinformatics. 2021;37(17):2747-2749. doi:10.1093/bioinformatics/btab079. PMID:33532816.