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.

PMID: 33532816
Funding: - ANR CoNoCo: ANR-18-CE12-0025

Links