Galaxy (computational biology)

Last updated
Galaxy
Developer(s) Galaxy Community
Initial release16 September 2005;19 years ago (2005-09-16)
Stable release
24.2 / January 2025 (2025-01)
Repository github.com/galaxyproject/galaxy
Written in Python, JavaScript, TypeScript
Operating system Unix-like
Platform Linux, macOS
Available inEnglish
Type Scientific workflow, data integration, analysis and data publishing
License MIT and Academic Free License [1]
Website galaxyproject.org

Galaxy [2] is an open-source scientific workflow system designed to make research accessible, reproducible, and transparent. Originally developed for computational biology, Galaxy has evolved into a domain-agnostic framework utilized across various scientific disciplines. Some examples include: data science, [3] microbiology, [4] medical research, [5] neuroscience, [6] virology [7] and outbreak detection, [8] food safety, [9] wastewater tracking and antibiotic resistance, [10] long-read [11] and high-throughput [12] genomic sequencing, bioinformatics, [13] and other scientific disciplines.

Contents

For many computational biology processes, Galaxy accommodates scientists from newcomers to professionals. It supports code-free workflow development, GUI workflow visualization as well as command-line interface access, scheduled jobs, and cloud infrastructure management. It supports data persistence and data publishing to facilitate collaboration. The freely hosted services of UseGalaxy (United States, EU, and Australia) support a global community of over 500,000 registered users through the Galaxy Hub which holds events, an annual conference, and hundreds of free online tutorials at the Galaxy Training Network.

Use

Use Areas

Galaxy was originally written for biological data analysis, particularly genomics. Tools on the platform are used for gene expression, genome assembly, epigenomics, transcriptomics, and host of other disciplines, in the life sciences. The wide set of available tools has been greatly expanded over the years because the platform is domain agnostic and can be applied to any scientific domain as a general bioinformatics workflow management system. [14] For example, Galaxy servers and tools exist for image analysis, [15] machine learning and AI, [16] computational chemistry [17] and drug design, [18] spaceflight [19] and astronomy, cheminformatics, [20] proteomics, social science, [21] and linguistics.

Use Cases

Here is a selection of a few recent use cases:

In 2021, members of the Galaxy team published a paper in Nature Biotechnology [22] detailing a method for tracking COVID-19 variants using Galaxy's scheduled jobs feature, Planemo, which is capable of processing and monitoring hundreds of thousands of samples.

In 2021, Galaxy partnered with the Vertebrate Genomes Project (VGP) which "aims to generate near error-free reference genome assemblies" [23] for approximately 70,000 vertebrate species.

In 2022, Goecks Lab introduced a scalable and modular pipeline, MCMICRO, which is capable of processing multiplexed imaging critical for analyzing complex tissue in cancer research and for improving precision oncology. [24]

See the Galaxy Google Scholar page and the Galaxy Zotero Group for additional key papers and citations

Project Goals

Galaxy is "an open, web-based platform for performing accessible, reproducible, and transparent genomic science." [25]

Accessibility

Computational biology is a specialized domain that often requires knowledge in computer programming. Galaxy provides biomedical researchers access to computational biology without requiring expertise in computer programming. [26] [27] To achieve this, Galaxy prioritizes a user-friendly interface [28] over the flexibility to construct highly complex workflows. This design choice makes it relatively easy to build typical analyses, but more difficult to build complex workflows that include, for example, looping constructs. (See Apache Taverna for an example of a data-driven workflow system that supports looping. [29] )

Reproducibility

Reproducibility is fundamental to science: when scientific results are published, they should include sufficient information for others to replicate the experiment and obtain the same results. In recent years, significant efforts have been made to extend this standard beyond traditional laboratory experiments (the "wet lab") to computational research (the "dry lab"). However, achieving reproducibility in computational experiments has proven more challenging than initially anticipated. [30]

Galaxy supports reproducibility by systematically capturing all essential details of a computational analysis, ensuring that it can be precisely replicated at any point in the future. This includes recording all input, intermediate, and final datasets, as well as the parameters used and the exact sequence of analytical steps.

Transparency

Transparency is essential in science, as it enables verification, fosters collaboration, and accelerates discoveries by allowing others to build upon existing work. Galaxy promotes transparency in scientific research by allowing researchers to share their GalaxyObjects either publicly or with specific individuals. Shared items can be thoroughly examined, rerun as needed, and copied or modified to explore new hypotheses.

Features

Tools

Galaxy is extensible, as new command line tools can be integrated and shared within the Galaxy ToolShed. [31] An example of extending Galaxy is Galaxy-P from the University of Minnesota Supercomputing Institute, which is customized as a data analysis platform for mass spectrometry-based proteomics. [32]
Galaxy provides a web interface for many text manipulation tools, enabling researchers to do their own custom reformatting and manipulation without having to know computer programming or shell scripting. Galaxy includes interval manipulation tools for doing set theoretic operations (e.g. intersection, union, ...) on intervals. Many biological file formats include genomic interval data (a frame of reference, e.g., chromosome or contig name, and start and stop positions), allowing these data to be integrated.

Galaxy Objects: Datasets, Workflows, Histories, and Pages

Galaxy objects are anything that can be saved, persisted, and shared in Galaxy:

Datasets

Datasets includes any input, intermediate, or output dataset, used or produced in an analysis. Galaxy's data integration platform supports file uploads from the user's computer, by URL, and directly from many online external resources (such as the UCSC Genome Browser, BioMart and InterMine). Galaxy supports a range of widely used biological data formats, translation between those formats, and data conversions (see Tools).

Workflows

Workflows are computational analyses that specify all the steps (and parameters) in the analysis, but none of the data. Workflows are used to run the same analysis against multiple sets of input data.
Galaxy is a scientific workflow system. These systems provide a means to build multi-step computational analyses akin to a recipe. They typically provide a graphical user interface [33] for specifying what data to operate on, what steps to take, and what order to do them in.

Histories

Histories are computational analyses (recipes) run with specified input datasets, computational steps and parameters. Histories include all intermediate and output datasets as well.

Pages

Pages enables the creation of a virtual paper that describes the how and why of the overall experiment. Histories, workflows and datasets can include user-provided annotation. Tight integration of Pages with Histories, Workflows, and Datasets supports this goal.

Availability

Galaxy is available:

  1. As a free public web server, [34] supported by the Galaxy Project. [35] This server includes many bioinformatics tools that are widely useful in many areas of genomics research. Users can create logins, and save histories, workflows, and datasets on the server. These saved items can also be shared with others.
  2. As open-source software that can be downloaded, installed and customized to address specific needs. [36] Galaxy can be installed locally or using a computing cloud. [37]
  3. Public web servers hosted by other organizations. [14] Several organizations with their own Galaxy installation have also opted to make those servers available to others.

Implementation

Galaxy is open-source software implemented using the Python programming language. It is developed by the Galaxy team [38] at Penn State, Johns Hopkins University, Oregon Health & Science University, Moffitt Cancer Center, Cleveland Clinic, University of Freiburg (Galaxy EU), and Galaxy Australia [39] [40] with community contributions from around the world.

Community

The Galaxy community is a global, interdisciplinary network of researchers, educators, and developers dedicated to making bioinformatics accessible, reproducible, and collaborative. With contributors from academia, government, and industry, the community actively develops new tools, maintains public Galaxy servers, and fosters open science initiatives. The Galaxy project has mailing lists, [41] a community hub, [35] and annual meetings. [42]

A key resource within this ecosystem is the Galaxy Training Network (GTN), [43] which provides comprehensive, open-access training materials for bioinformatics workflows and computational biology. The GTN offers hands-on tutorials covering a wide range of topics, from sequencing data analysis to machine learning applications in genomics. These materials are regularly updated and include step-by-step instructions for using Galaxy along with interactive Jupyter-based lessons, making them valuable for both self-guided learning and structured coursework. Through events like the annual Galaxy Community Conference (GCC), the annual Galaxy Training Academy, hackathons, and collaborative projects, the Galaxy community continues to expand its impact, ensuring that cutting-edge bioinformatics tools remain accessible to all researchers, regardless of their computational expertise. See the Galaxy events page for more information

See also

References

  1. ↑ "Project Licenses". GitHub .
  2. ↑ The Galaxy Community (20 May 2024). "The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update". Nucleic Acids Research. 52 (Web Server Issue): W83 –W94. doi: 10.1093/nar/gkae410 . PMC   11223835 . PMID   38769056.
  3. ↑ Pireddu, Luca; Leo, Simone; Soranzo, Nicola; Zanetti, Gianluigi (2014-09-20). "A Hadoop-Galaxy adapter for user-friendly and scalable data-intensive bioinformatics in Galaxy". Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. BCB '14. New York, NY, USA: Association for Computing Machinery. pp. 184–191. doi:10.1145/2649387.2649429. ISBN   978-1-4503-2894-4.
  4. ↑ Nasr, Engy; Amato, Pierre; Bhardwaj, Anshu; Blankenberg, Daniel; Brites, Daniela; Cumbo, Fabio; Do, Katherine; Ferrari, Emanuele; Griffin, Timothy J. (2024-12-27). "microGalaxy: A gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data". bioRxiv   10.1101/2024.12.23.629682 .
  5. ↑ Foschini, Maria P.; Morandi, Luca; Sanchez, Alejandro M.; Santoro, Angela; Mulè, Antonino; Zannoni, Gian Franco; Varga, Zsuzsanna; Moskovszky, Linda; Cucchi, Maria C.; Moelans, Cathy B.; Giove, Gianluca; van Diest, Paul J.; Masetti, Riccardo (2020-06-17). "Methylation Profile of X-Chromosome–Related Genes in Male Breast Cancer". Frontiers in Oncology. 10: 784. doi: 10.3389/fonc.2020.00784 . ISSN   2234-943X. PMC   7313421 . PMID   32626651.
  6. ↑ Mitolo, Micaela; Zoli, Matteo; Testa, Claudia; Morandi, Luca; Rochat, Magali Jane; Zaccagna, Fulvio; Martinoni, Matteo; Santoro, Francesca; Asioli, Sofia; Badaloni, Filippo; Conti, Alfredo; Sturiale, Carmelo; Lodi, Raffaele; Mazzatenta, Diego; Tonon, Caterina (2022-06-03). "Neuroplasticity Mechanisms in Frontal Brain Gliomas: A Preliminary Study". Frontiers in Neurology. 13. doi: 10.3389/fneur.2022.867048 . ISSN   1664-2295. PMC   9204970 . PMID   35720068.
  7. ↑ Lo, Chien-Chi; Shakya, Migun; Connor, Ryan; Davenport, Karen; Flynn, Mark; Gutiérrez, Adán Myers y; Hu, Bin; Li, Po-E; Jackson, Elais Player; Xu, Yan; Chain, Patrick S G (2022-05-13). "EDGE COVID-19: a web platform to generate submission-ready genomes from SARS-CoV-2 sequencing efforts". Bioinformatics. 38 (10): 2700–2704. doi:10.1093/bioinformatics/btac176. ISSN   1367-4803. PMC   9113274 . PMID   35561186.
  8. ↑ Bogaerts, Bert; Van Braekel, Julien; Van Uffelen, Alexander; D’aes, Jolien; Godfroid, Maxime; Delcourt, Thomas; Kelchtermans, Michael; Milis, Kato; Goeders, Nathalie; De Keersmaecker, Sigrid C. J.; Roosens, Nancy H. C.; Winand, Raf; Vanneste, Kevin (2025-01-08). "Galaxy @Sciensano: a comprehensive bioinformatics portal for genomics-based microbial typing, characterization, and outbreak detection". BMC Genomics. 26 (1): 20. doi: 10.1186/s12864-024-11182-5 . ISSN   1471-2164. PMC   11715294 . PMID   39780046.
  9. ↑ Gangiredla, Jayanthi; Rand, Hugh; Benisatto, Daniel; Payne, Justin; Strittmatter, Charles; Sanders, Jimmy; Wolfgang, William J.; Libuit, Kevin; Herrick, James B.; Prarat, Melanie; Toro, Magaly; Farrell, Thomas; Strain, Errol (2021-02-10). "GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians". BMC Genomics. 22 (1): 114. doi: 10.1186/s12864-021-07405-8 . ISSN   1471-2164. PMC   7877046 . PMID   33568057.
  10. ↑ Yin, Xiaole; Zheng, Xiawan; Li, Liguan; Zhang, An-Ni; Jiang, Xiao-Tao; Zhang, Tong (2023-08-01). "ARGs-OAP v3.0: Antibiotic-Resistance Gene Database Curation and Analysis Pipeline Optimization". Engineering. 27: 234–241. Bibcode:2023Engin..27..234Y. doi: 10.1016/j.eng.2022.10.011 . ISSN   2095-8099.
  11. ↑ de Koning, Willem; Miladi, Milad; Hiltemann, Saskia; Heikema, Astrid; Hays, John P; Flemming, Stephan; van den Beek, Marius; Mustafa, Dana A; Backofen, Rolf; Grüning, Björn; Stubbs, Andrew P (2020-10-20). "NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy". GigaScience. 9 (10): giaa105. doi:10.1093/gigascience/giaa105. ISSN   2047-217X. PMC   7568507 . PMID   33068114.
  12. ↑ Thiel, William H (2016). "Galaxy Workflows for Web-based Bioinformatics Analysis of Aptamer High-throughput Sequencing Data". Molecular Therapy - Nucleic Acids. 5 (8): e345. doi:10.1038/mtna.2016.54. PMC   5023399 . PMID   28131286.
  13. ↑ Thiel, William H.; Giangrande, Paloma H. (2016-03-15). "Analyzing HT-SELEX data with the Galaxy Project tools – A web based bioinformatics platform for biomedical research". Methods. Nucleic Acid Aptamers. 97: 3–10. doi:10.1016/j.ymeth.2015.10.008. ISSN   1046-2023. PMC   4792767 . PMID   26481156.
  14. 1 2 "Galaxy Community Hub - Galaxy Platform Directory".
  15. ↑ "biotools Galaxy Image Analysis".
  16. ↑ "Galaxy Machine Learning". ml.usegalaxy.eu. Retrieved 2025-03-02.
  17. ↑ Hildebrandt, A. K.; Stöckel, D; Fischer, N. M.; de la Garza, L; Krüger, J; Nickels, S; Röttig, M; Schärfe, C; Schumann, M; Thiel, P; Lenhof, H. P.; Kohlbacher, O; Hildebrandt, A (2014). "Ballaxy: Web services for structural bioinformatics". Bioinformatics. 31 (1): 121–2. doi: 10.1093/bioinformatics/btu574 . PMID   25183489.
  18. ↑ "OSDDlinux". Archived from the original on 2016-05-07. Retrieved 2014-11-17.
  19. ↑ "GeneLab - Galaxy Community Hub". Galaxy Community Hub. October 4, 2021. Retrieved March 2, 2025.
  20. ↑ Bray, Simon A.; Lucas, Xavier; Kumar, Anup; Grüning, Björn A. (1 June 2020). "The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform". Journal of Cheminformatics. 12 (1): 40. doi: 10.1186/s13321-020-00442-7 . PMC   7268608 . PMID   33431029.
  21. ↑ "Galaxy".
  22. ↑ Maier, Wolfgang; Bray, Simon; van den Beek, Marius; Bouvier, Dave; Coraor, Nathan; Miladi, Milad; Singh, Babita; De Argila, Jordi Rambla; Baker, Dannon; Roach, Nathan; Gladman, Simon; Coppens, Frederik; Martin, Darren P.; Lonie, Andrew; Grüning, Björn (October 2021). "Ready-to-use public infrastructure for global SARS-CoV-2 monitoring". Nature Biotechnology. 39 (10): 1178–1179. doi:10.1038/s41587-021-01069-1. ISSN   1546-1696. PMC   8845060 . PMID   34588690.
  23. ↑ "Vertebrate Genomes Project". Vertebrate Genomes Project. 2023-04-03. Retrieved 2025-02-21.
  24. ↑ Schapiro, Denis; Sokolov, Artem; Yapp, Clarence; Chen, Yu-An; Muhlich, Jeremy L.; Hess, Joshua; Creason, Allison L.; Nirmal, Ajit J.; Baker, Gregory J.; Nariya, Maulik K.; Lin, Jia-Ren; Maliga, Zoltan; Jacobson, Connor A.; Hodgman, Matthew W.; Ruokonen, Juha (March 2022). "MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging". Nature Methods. 19 (3): 311–315. doi:10.1038/s41592-021-01308-y. ISSN   1548-7105. PMC   8916956 . PMID   34824477.
  25. ↑ Goecks, J.; Nekrutenko, A.; Taylor, J.; Galaxy Team, T. (2010). "Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences". Genome Biology. 11 (8): R86. doi: 10.1186/gb-2010-11-8-r86 . PMC   2945788 . PMID   20738864.
  26. ↑ Blankenberg, D.; Taylor, J.; Nekrutenko, A.; The Galaxy, T. (2011). "Making whole genome multiple alignments usable for biologists". Bioinformatics. 27 (17): 2426–8. doi:10.1093/bioinformatics/btr398. PMC   3157923 . PMID   21775304.
  27. ↑ Blankenberg, D.; Taylor, J.; Schenck, I.; He, J.; Zhang, Y.; Ghent, M.; Veeraraghavan, N.; Albert, I.; Miller, W.; Makova, K. D.; Hardison, R. C.; Nekrutenko, A. (2007). "A framework for collaborative analysis of ENCODE data: Making large-scale analyses biologist-friendly". Genome Research. 17 (6): 960–964. doi:10.1101/gr.5578007. PMC   1891355 . PMID   17568012.
  28. ↑ Schatz, M. C. (2010). "The missing graphical user interface for genomics". Genome Biology. 11 (8): 128–201. doi: 10.1186/gb-2010-11-8-128 . PMC   2945776 . PMID   20804568.
  29. ↑ Soiland-Reyes, S (2010-12-13). "Looping". The Taverna Knowledge Blog. knowledgeblog.org. Archived from the original on 30 December 2016. Retrieved 28 January 2015.
  30. ↑ Ioannidis, J. P. A.; Allison, D. B.; Ball, C. A.; Coulibaly, I.; Cui, X.; Culhane, A. N. C.; Falchi, M.; Furlanello, C.; Game, L.; Jurman, G.; Mangion, J.; Mehta, T.; Nitzberg, M.; Page, G. P.; Petretto, E.; Van Noort, V. (2008). "Repeatability of published microarray gene expression analyses". Nature Genetics. 41 (2): 149–155. doi:10.1038/ng.295. PMID   19174838. S2CID   5153795.
  31. ↑ Blankenberg, Daniel; Von Kuster, Gregory; Bouvier, Emil; Baker, Dannon; Afgan, Enis; Stoler, Nicholas; Taylor, James; Nekrutenko, Anton (2014). "Dissemination of scientific software with Galaxy ToolShed". Genome Biology. 15 (2): 403. doi: 10.1186/gb4161 . PMC   4038738 . PMID   25001293.
  32. ↑ Sheynkman, GM; Johnson, JE; Jagtap, PD; Shortreed, MR; Onsongo, G; Frey, BL; Griffin, TJ; Smith, LM (22 August 2014). "Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations". BMC Genomics. 15 (703): 703. doi: 10.1186/1471-2164-15-703 . PMC   4158061 . PMID   25149441.
  33. ↑ Schatz, M. C. (2010). "The missing graphical user interface for genomics". Genome Biology. 11 (8): 128–201. doi: 10.1186/gb-2010-11-8-128 . PMC   2945776 . PMID   20804568.
  34. ↑ "usegalaxy.org: Main instance of Galaxy in the United States"
  35. 1 2 "Galaxy Community Hub"
  36. ↑ "getgalaxy.org: How to get Galaxy"
  37. ↑ Afgan, E.; Baker, D.; Coraor, N.; Chapman, B.; Nekrutenko, A.; Taylor, J. (2010). "Galaxy CloudMan: Delivering cloud compute clusters". BMC Bioinformatics. 11 (Suppl 12): S4. doi: 10.1186/1471-2105-11-S12-S4 . PMC   3040530 . PMID   21210983.
  38. ↑ "Galaxy Community Hub - Team".
  39. ↑ "Galaxy Australia Media". site.usegalaxy.org.au. Retrieved 2025-02-21.
  40. ↑ Lazarus, R.; Taylor, J.; Qiu, W.; Nekrutenko, A. (2008). "Toward the commoditization of translational genomic research: Design and implementation features of the Galaxy genomic workbench". Summit on Translational Bioinformatics. 2008: 56–60. PMC   3041519 . PMID   21347127.
  41. ↑ "Galaxy Mailing Lists".
  42. ↑ "Galaxy Community Conferences (GCCs)".
  43. ↑ Hiltemann, Saskia; Rasche, Helena; Gladman, Simon; Hotz, Hans-Rudolf; Larivière, Delphine; Blankenberg, Daniel; Jagtap, Pratik D.; Wollmann, Thomas; Bretaudeau, Anthony; Goué, Nadia; Griffin, Timothy J.; Royaux, Coline; Le Bras, Yvan; Mehta, Subina; Syme, Anna; Coppens, Frederik; Droesbeke, Bert; Soranzo, Nicola; Bacon, Wendi; Psomopoulos, Fotis; Gallardo-Alba, Cristóbal; Davis, John; Föll, Melanie Christine; Fahrner, Matthias; Doyle, Maria A.; Serrano-Solano, Beatriz; Fouilloux, Anne Claire; van Heusden, Peter; Maier, Wolfgang; Clements, Dave; Heyl, Florian; Galaxy Training Network; Grüning, Björn; Batut, Bérénice (2023-01-09). "Galaxy Training: A powerful framework for teaching!". PLOS Computational Biology. 19 (1): e1010752. doi: 10.1371/journal.pcbi.1010752 . ISSN   1553-7358. PMC   9829167 . PMID   36622853.
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