Galaxy Index

Galaxy Project

Galaxy Project is an open-source, web-based platform for accessible, reproducible, and transparent computational biomedical research, especially in bioinformatics. It enables users to perform complex analyses without requiring advanced programming skills.

Typical Use Cases

  • RNA-seq analysis (alignment, quantification, differential expression)
  • Variant calling (SNP/indel detection)
  • Metagenomics and microbiome profiling
  • Epigenomics (ChIP-seq, methylation analysis)
  • Genome assembly and annotation

Key Advantages

  • Low barrier to entry for beginners
  • Strong reproducibility and provenance tracking
  • Large community and training ecosystem

Galaxy Project Resources

  • Main Project Website
    Galaxy Project
    The official community hub of Galaxy, providing platform overview, news, documentation, and community activities. Galaxy is an open-source scientific workflow system designed for accessible, reproducible, and transparent computational research across life sciences and other domains.

  • Galaxy (Public Server — USA)
    usegalaxy.org
    The primary public Galaxy server in the United States, maintained by the Galaxy Project team and freely available worldwide. It supports 10,000+ tools for genomics, transcriptomics, comparative genomics, and more. Users can store histories, workflows, and datasets, and share analyses with collaborators.

  • Galaxy Tool Shed (Tools Repository)
    toolshed.g2.bx.psu.edu
    The official Galaxy tools repository (analogous to an “app store”), maintained by the Galaxy team. Developers can publish, version, and share tool wrappers, data managers, and custom datatypes. Administrators can install or update tools directly into local Galaxy instances.

  • Galaxy Training Network (GTN)
    training.galaxyproject.org
    The Galaxy Training Network provides 400+ open tutorials across 25 scientific and 6 technical topics, including transcriptomics, metagenomics, single-cell analysis, genome assembly, and epigenomics. Materials are contributed by 325+ contributors and include step-by-step instructions with real datasets, suitable for self-learning and teaching.