Galaxy (computational biology)

Last updated
Galaxy
Developer(s) Galaxy Community
Initial release16 September 2005;18 years ago (2005-09-16)
Stable release
23.1 / June 2023 (2023-06)
Repository github.com/galaxyproject/galaxy
Written in Python, JavaScript
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 a scientific workflow, data integration, [3] [4] and data and analysis persistence and publishing platform that aims to make computational biology accessible to research scientists that do not have computer programming or systems administration experience. Although it was initially developed for genomics research, it is largely domain agnostic and is now used as a general bioinformatics workflow management system. [5]

Contents

Functionality

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 [6] for specifying what data to operate on, what steps to take, and what order to do them in.

Galaxy is also a data integration platform for biological data. It supports data uploads from the user's computer, by URL, and directly from many online resources (such as the UCSC Genome Browser, BioMart and InterMine). Galaxy supports a range of widely used biological data formats, and translation between those formats. Galaxy provides a web interface to many text manipulation utilities, enabling researchers to do their own custom reformatting and manipulation without having to do any programming. Galaxy includes interval manipulation utilities 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 was originally written for biological data analysis, particularly genomics. The set of available tools has been greatly expanded over the years and Galaxy is now also used for gene expression, genome assembly, proteomics, epigenomics, transcriptomics and host of other disciplines in the life sciences. The platform itself is actually domain agnostic and can be applied, in theory, to any scientific domain, such as cheminformatics. [7] For example, Galaxy servers exist for image analysis, [8] computational chemistry [9] and drug design, [10] cosmology, climate modeling, social science, [11] and linguistics.

Finally, Galaxy also supports data and analysis persistence and publishing. See Reproducibility and Transparency below.

Project Goals

Galaxy is "an open, web-based platform for performing accessible, reproducible, and transparent genomic science." [12]

Accessibility

Computational biology is a specialized domain that often requires knowledge of computer programming. Galaxy aims to give biomedical researchers access to computational biology without also requiring them to understand computer programming. [13] [14] Galaxy does this by stressing a simple user interface [15] over the ability to build 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. [16] )

Reproducibility

Reproducibility is a key goal of science: When scientific results are published the publications should include enough information that others can repeat the experiment and get the same results. There have been many recent efforts to extend this goal from the bench (the "wet lab") to computational experiments (the "dry lab") as well. This has proved to be a more difficult task than initially expected. [17]

Galaxy supports reproducibility by capturing sufficient information about every step in a computational analysis, so that the analysis can be repeated, exactly, at any point in the future. This includes keeping track of all input, intermediate, and final datasets, as well as the parameters provided to, and the order of each step of the analysis.

Transparency

Galaxy supports transparency in scientific research by enabling researchers to share any of their Galaxy Objects either publicly, or with specific individuals. Shared items can be examined in detail, rerun at will and copied and modified to test hypotheses.

Galaxy Objects: Histories, Workflows, Datasets and Pages

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

Histories
Histories are computational analyses (recipes) run with specified input datasets, computational steps and parameters. Histories include all intermediate and output datasets as well.
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.
Datasets
Datasets includes any input, intermediate, or output dataset, used or produced in an analysis.
Pages
Histories, workflows and datasets can include user-provided annotation. Galaxy Pages enables the creation of a virtual paper that describes the how and why of the overall experiment. Tight integration of Pages with Histories, Workflows, and Datasets supports this goal.

Availability

Galaxy is available:

  1. As a free public web server, [18] supported by the Galaxy Project. [19] 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. [20] Galaxy can be installed locally or using a computing cloud. [21]
  3. Public web servers hosted by other organizations. [22] 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 [23] at Penn State, Johns Hopkins University, Oregon Health & Science University, and the Galaxy Community. [24]

Galaxy is extensible, as new command line tools can be integrated and shared within the Galaxy ToolShed. [25]

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. [26]

Community

Galaxy is an open source project and the community includes users, organizations that install their own instance, Galaxy developers, and bioinformatics tool developers. The Galaxy project has mailing lists, [27] a community hub, [28] and annual meetings. [29]

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

In bioinformatics and biochemistry, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes.

<span class="mw-page-title-main">Metagenomics</span> Study of genes found in the environment

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.

Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology.

<span class="mw-page-title-main">Generic Model Organism Database</span>

The Generic Model Organism Database (GMOD) project provides biological research communities with a toolkit of open-source software components for visualizing, annotating, managing, and storing biological data. The GMOD project is funded by the United States National Institutes of Health, National Science Foundation and the USDA Agricultural Research Service.

GenePattern is a freely available computational biology open-source software package originally created and developed at the Broad Institute for the analysis of genomic data. Designed to enable researchers to develop, capture, and reproduce genomic analysis methodologies, GenePattern was first released in 2004. GenePattern is currently developed at the University of California, San Diego.

<span class="mw-page-title-main">Apache Taverna</span>

Apache Taverna was an open source software tool for designing and executing workflows, initially created by the myGrid project under the name Taverna Workbench, then a project under the Apache incubator. Taverna allowed users to integrate many different software components, including WSDL SOAP or REST Web services, such as those provided by the National Center for Biotechnology Information, the European Bioinformatics Institute, the DNA Databank of Japan (DDBJ), SoapLab, BioMOBY and EMBOSS. The set of available services was not finite and users could import new service descriptions into the Taverna Workbench.

Mark Bender Gerstein is an American scientist working in bioinformatics and Data Science. As of 2009, he is co-director of the Yale Computational Biology and Bioinformatics program.

<span class="mw-page-title-main">Pan-genome</span> All genes of all strains in a clade

In the fields of molecular biology and genetics, a pan-genome is the entire set of genes from all strains within a clade. More generally, it is the union of all the genomes of a clade. The pan-genome can be broken down into a "core pangenome" that contains genes present in all individuals, a "shell pangenome" that contains genes present in two or more strains, and a "cloud pangenome" that contains genes only found in a single strain. Some authors also refer to the cloud genome as "accessory genome" containing 'dispensable' genes present in a subset of the strains and strain-specific genes. Note that the use of the term 'dispensable' has been questioned, at least in plant genomes, as accessory genes play "an important role in genome evolution and in the complex interplay between the genome and the environment". The field of study of pangenomes is called pangenomics.

<span class="mw-page-title-main">Robert Gentleman (statistician)</span> Canadian statistician

Robert Clifford Gentleman is a Canadian statistician and bioinformatician who is currently the founding executive director of the Center for Computational Biomedicine at Harvard Medical School. He was previously the vice president of computational biology at 23andMe. Gentleman is recognized, along with Ross Ihaka, as one of the originators of the R programming language and the Bioconductor project.

The Genomic HyperBrowser is a web-based system for statistical analysis of genomic annotation data.

<span class="mw-page-title-main">BioMart</span>

BioMart is a community-driven project to provide a single point of access to distributed research data. The BioMart project contributes open source software and data services to the international scientific community. Although the BioMart software is primarily used by the biomedical research community, it is designed in such a way that any type of data can be incorporated into the BioMart framework. The BioMart project originated at the European Bioinformatics Institute as a data management solution for the Human Genome Project. Since then, BioMart has grown to become a multi-institute collaboration involving various database projects on five continents.

GenomeSpace is an environment for genomics software tools and applications. It helps users manage their analysis workflows involving multiple diverse tools, including web applications and desktop tools and facilitates the transfer of data between tools via automatic format conversion. Analyses can use data from local or cloud-based stores.

A bioinformatics workflow management system is a specialized form of workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, that relate to bioinformatics.

MG-RAST is an open-source web application server that suggests automatic phylogenetic and functional analysis of metagenomes. It is also one of the biggest repositories for metagenomic data. The name is an abbreviation of Metagenomic Rapid Annotations using Subsystems Technology. The pipeline automatically produces functional assignments to the sequences that belong to the metagenome by performing sequence comparisons to databases in both nucleotide and amino-acid levels. The applications supply phylogenetic and functional assignments of the metagenome being analysed, as well as tools for comparing different metagenomes. It also provides a RESTful API for programmatic access.

<span class="mw-page-title-main">Gene set enrichment analysis</span> Bioinformatics method

Gene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g. different organism growth patterns or diseases). The method uses statistical approaches to identify significantly enriched or depleted groups of genes. Transcriptomics technologies and proteomics results often identify thousands of genes, which are used for the analysis.

The BioCompute Object (BCO) project is a community-driven initiative to build a framework for standardizing and sharing computations and analyses generated from High-throughput sequencing. The project has since been standardized as IEEE 2791-2020, and the project files are maintained in an open source repository. The July 22nd, 2020 edition of the Federal Register announced that the FDA now supports the use of BioCompute in regulatory submissions, and the inclusion of the standard in the Data Standards Catalog for the submission of HTS data in NDAs, ANDAs, BLAs, and INDs to CBER, CDER, and CFSAN.

Originally started as a collaborative contract between the George Washington University and the Food and Drug Administration, the project has grown to include over 20 universities, biotechnology companies, public-private partnerships and pharmaceutical companies including Seven Bridges and Harvard Medical School. The BCO aims to ease the exchange of HTS workflows between various organizations, such as the FDA, pharmaceutical companies, contract research organizations, bioinformatic platform providers, and academic researchers. Due to the sensitive nature of regulatory filings, few direct references to material can be published. However, the project is currently funded to train FDA Reviewers and administrators to read and interpret BCOs, and currently has 4 publications either submitted or nearly submitted.

The 'German Network for Bioinformatics Infrastructure – de.NBI' is a national, academic and non-profit infrastructure initiated by the Federal Ministry of Education and Research funding 2015-2021. The network provides bioinformatics services to users in life sciences research and biomedicine in Germany and Europe. The partners organize training events, courses and summer schools on tools, standards and compute services provided by de.NBI to assist researchers to more effectively exploit their data. From 2022, the network will be integrated into Forschungszentrum Jülich.

Nextflow is a scientific workflow system predominantly used for bioinformatic data analyses. It imposes standards on how to programmatically author a sequence of dependent compute steps and enables their execution on various local and cloud resources. Nextflow was conceived at the Centre for Genomic Regulation in Barcelona, Spain, but has since found world-wide adoption in biomedical and genomics research facilities and laboratories.

References

  1. ↑ "Project Licenses". GitHub .
  2. ↑ Afgan, E.; Baker, D.; van den Beek, M.; Blankenberg, D.; Bouvier, D.; Čech, M.; Chilton, J.; Clements, D.; Coraor, N.; Eberhard, C.; Grüning, B.; Guerler, A.; Hillman-Jackson, J.; Von Kuster, G.; Rasche, E.; Soranzo, N.; Turaga, N.; Taylor, J.; Nekrutenko, A.; Goecks, J. (8 July 2016). "The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update". Nucleic Acids Research. 44 (W1): W3–W10. doi:10.1093/nar/gkw343. PMC   4987906 . PMID   27137889.
  3. ↑ Blankenberg, D.; Coraor, N.; Von Kuster, G.; Taylor, J.; Nekrutenko, A.; Galaxy, T. (2011). "Integrating diverse databases into an unified analysis framework: A Galaxy approach". Database. 2011: bar011. doi:10.1093/database/bar011. PMC   3092608 . PMID   21531983.
  4. ↑ Blankenberg, D.; Gordon, A.; Von Kuster, G.; Coraor, N.; Taylor, J.; Nekrutenko, A.; Galaxy, T. (2010). "Manipulation of FASTQ data with Galaxy". Bioinformatics. 26 (14): 1783–1785. doi:10.1093/bioinformatics/btq281. PMC   2894519 . PMID   20562416.
  5. ↑ "Galaxy Community Hub - Galaxy Community Hub".
  6. ↑ 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.
  7. ↑ 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.
  8. ↑ "biotools Galaxy Image Analysis".
  9. ↑ 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.
  10. ↑ "OSDDlinux". Archived from the original on 2016-05-07. Retrieved 2014-11-17.
  11. ↑ "Galaxy".
  12. ↑ 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.
  13. ↑ 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.
  14. ↑ 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.
  15. ↑ 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.
  16. ↑ 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.
  17. ↑ 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.
  18. ↑ "usegalaxy.org: Main instance of Galaxy in the United States"
  19. ↑ "galaxyproject.org: Galaxy Community Hub"
  20. ↑ "getgalaxy.org: How to get Galaxy"
  21. ↑ 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.
  22. ↑ "Galaxy Community Hub - Galaxy Community Hub".
  23. ↑ "Galaxy Community Hub - Galaxy Community Hub".
  24. ↑ 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.
  25. ↑ 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.
  26. ↑ 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.
  27. ↑ "Galaxy Mailing Lists".
  28. ↑ "galaxyproject.org: Galaxy Community Hub
  29. ↑ "Galaxy Community Conferences (GCCS)".
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