Enis AfganpersName, Dannon BakerpersName, Bérénice BatutpersName, Marius van den BeekpersName, Dave BouvierpersName, Martin ČechpersName, John ChiltonpersName, Dave ClementspersName, Nate CoraorpersName, Björn A GrüningpersName, Aysam GuerlerpersName, Jennifer Hillman-JacksonpersName, Saskia HiltemannpersName, Vahid JalilipersName, Helena RaschepersName, Nicola SoranzopersName, Jeremy GoeckspersName, James TaylorpersName, Anton NekrutenkopersName, Daniel BlankenbergpersName (2018date). “The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.” Nucleic Acids Researchtitle·j.
Object of study — the 2018date architecture, scalabilityterm, tools, interfaces, workflowsterm, infrastructure, and communities of the Galaxyterm biomedical-analysis platform
Research question — how Galaxyterm evolved to make increasingly large biomedical analyses accessible, reproducible, scalable, shareable, and extensible
Contribution — reports and organizes two years of Galaxyterm improvements across frontend and backend scalabilityterm, tools, workflowsterm, interactive environments, deployment, reference data, training, and community development
Cross-disciplinary analysis
content-tags & facets:
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | the growth of large biomedical datasets and communities beyond the scale manageable by earlier analysis interfaces and server infrastructure — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | |
| claim-type | existence — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | |
| method — evidence | — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | |
| object of study — evidence | — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | |
| research question — evidence | — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | |
| contribution — evidence | — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | |
| mechanism | accessible interfaces, platform-independent histories, and transparent sharing make analyses reusable and extensible — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | mechanisms[0] |
| mechanism | workflow and collection operations let the browser manipulate thousands of datasets as coordinated units — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | mechanisms[1] |
| mechanism | job managers dynamically dispatch atomic work across local, institutional, cloud, and national resources — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | mechanisms[2] |
| mechanism | cloud abstraction separates Galaxy launch logic from provider-specific machine images and file systems — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | mechanisms[3] |
| mechanism | tool profile versions preserve compatibility while allowing defaults and behaviors to evolve — “The portability and backward-compatibility of the Galaxy tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile version , which is used to ensure compatibility between a version of a tool and its targeted Galaxy version. In addition, tool profile versions allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflow. We improved the installation process so that restarting Galaxy is not required to use a newly installed tool.” | mechanisms[4] |
| mechanism | interactive environments combine managed Galaxy infrastructure with ad hoc scripting — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | mechanisms[5] |
| mechanism | named-tag propagation maintains dataset identity through large workflows — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | mechanisms[6] |
| mechanism | nested dataset collections convert repeated per-dataset operations into batch execution — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | mechanisms[7] |
| mechanism | CVMFS distributes one versioned read-only reference collection to many Galaxy instances — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | mechanisms[8] |
| mechanism | Conda and containers provision portable reproducible dependency environments — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | mechanisms[9] |
| mechanism | reviewed Markdown pull requests automatically regenerate a community-maintained static Hub — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | mechanisms[10] |
| mechanism | continuous integration runs comprehensive suites on each proposed change to reduce regression risk — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | mechanisms[11] |
| extends | Galaxy's framework, interface, tools, training, scalability, and communities since the prior update — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | claim_relations[0] |
| supports | accessible, reproducible, transparent, reusable biomedical analysis — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | claim_relations[1] |
| validates | frontend scalability on an 11,872-history-item single-cell workflow — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | claim_relations[2] |
| demonstrates | larger and isolated jobs through national and cloud cyberinfrastructure — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | claim_relations[3] |
| introduces | Galaxy Interactive Environments integrating graphical analysis and ad hoc scripting — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | claim_relations[4] |
| extends | workflows with nesting, background scheduling, version changes, and Common Workflow Language support — “Galaxy workflows have been extended in several ways. Switching between tool versions and upgrading workflows with new tool versions is now supported. A workflow can now be embedded in another, making it easier to create and edit workflows that have many common steps repeated. Many of these features have existed in in standalone workflow systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxy users. Workflows are now scheduled by a Galaxy server more efficiently and in the background, making it possible to execute larger workflows, generating tens of thousands of jobs, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxy with initial support for running workflows defined in the Common Workflow Language ( 32 ) format.” | claim_relations[5] |
| supports | portable reproducible tool execution using Conda and container technologies — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | claim_relations[6] |
| builds-on | open-source projects and community-authored tools, documentation, and infrastructure — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | claim_relations[7] |
| validates | automated test execution as a protection against codebase regressions — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[8] |
| qualifies | Galaxy's current platform completeness — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[9] |
| sense | Galaxy - a web platform unifying accessible analysis, reproducibility, communication, reuse, and extension — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | senses[0] |
| sense | Galaxy Project - four complementary components: public server, open framework, ToolShed, and stakeholder community — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | senses[1] |
| sense | User interface scalability - many-dataset interaction and pipeline design without overwhelming the web frontend — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | senses[2] |
| sense | Server scalability - multiuser job execution across laptops, clusters, clouds, and national infrastructure — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | senses[3] |
| sense | Galaxy ToolShed - an AppStore-like community exchange for installable Galaxy tools, workflows, and visualizations — “The Galaxy ToolShed ( 11 ) assumes the role of an AppStore for Galaxy instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxy servers and computing environments.” | senses[4] |
| sense | Galaxy Interactive Environments - a combined graphical and ad hoc scripting mode using shared Galaxy infrastructure — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | senses[5] |
| sense | named-tag - a hash-prefixed persistent dataset identifier propagated through the analysis lifecycle — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | senses[6] |
| sense | Galaxy Dataset Collections - nested dataset containers supporting simultaneous analysis and batch execution — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | senses[7] |
| sense | Galaxy flavors - personalized, preconfigured, self-contained, portable deployments built from Galaxy-Docker — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | senses[8] |
| sense | Intergalactic Utilities Commission - a self-regulating community body defining and verifying high-quality ToolShed practices — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | senses[9] |
| note | Galaxy's framework, interface, tools, training, scalability, and communities since the prior update — The paper is explicitly a feature and growth update. — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | claim_relations[0] |
| weight | Galaxy's framework, interface, tools, training, scalability, and communities since the prior update — 0.99 — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | claim_relations[0] |
| note | accessible, reproducible, transparent, reusable biomedical analysis — The three goals are stated directly. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | claim_relations[1] |
| weight | accessible, reproducible, transparent, reusable biomedical analysis — 0.99 — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | claim_relations[1] |
| note | frontend scalability on an 11,872-history-item single-cell workflow — The authors describe a concrete benchmark against earlier versions. — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | claim_relations[2] |
| weight | frontend scalability on an 11,872-history-item single-cell workflow — 0.99 — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | claim_relations[2] |
| note | larger and isolated jobs through national and cloud cyberinfrastructure — The passage reports both increased job size and new interactive job types. — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | claim_relations[3] |
| weight | larger and isolated jobs through national and cloud cyberinfrastructure — 0.99 — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | claim_relations[3] |
| note | Galaxy Interactive Environments integrating graphical analysis and ad hoc scripting — The article says the environments were recently introduced. — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | claim_relations[4] |
| weight | Galaxy Interactive Environments integrating graphical analysis and ad hoc scripting — 0.99 — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | claim_relations[4] |
| note | workflows with nesting, background scheduling, version changes, and Common Workflow Language support — The update enumerates these extensions. — “Galaxy workflows have been extended in several ways. Switching between tool versions and upgrading workflows with new tool versions is now supported. A workflow can now be embedded in another, making it easier to create and edit workflows that have many common steps repeated. Many of these features have existed in in standalone workflow systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxy users. Workflows are now scheduled by a Galaxy server more efficiently and in the background, making it possible to execute larger workflows, generating tens of thousands of jobs, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxy with initial support for running workflows defined in the Common Workflow Language ( 32 ) format.” | claim_relations[5] |
| weight | workflows with nesting, background scheduling, version changes, and Common Workflow Language support — 0.99 — “Galaxy workflows have been extended in several ways. Switching between tool versions and upgrading workflows with new tool versions is now supported. A workflow can now be embedded in another, making it easier to create and edit workflows that have many common steps repeated. Many of these features have existed in in standalone workflow systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxy users. Workflows are now scheduled by a Galaxy server more efficiently and in the background, making it possible to execute larger workflows, generating tens of thousands of jobs, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxy with initial support for running workflows defined in the Common Workflow Language ( 32 ) format.” | claim_relations[5] |
| note | portable reproducible tool execution using Conda and container technologies — The dependency and container stack is described directly. — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | claim_relations[6] |
| weight | portable reproducible tool execution using Conda and container technologies — 0.99 — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | claim_relations[6] |
| note | open-source projects and community-authored tools, documentation, and infrastructure — The Hub and platform are maintained through reviewable community contributions. — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | claim_relations[7] |
| weight | open-source projects and community-authored tools, documentation, and infrastructure — 0.95 — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | claim_relations[7] |
| note | automated test execution as a protection against codebase regressions — The development process runs comprehensive suites on every proposed change. — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[8] |
| weight | automated test execution as a protection against codebase regressions — 0.95 — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[8] |
| note | Galaxy's current platform completeness — The paper closes with a substantial list of future directions. — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[9] |
| weight | Galaxy's current platform completeness — 0.9 — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | claim_relations[9] |
| note | Galaxy —is-a→ web-based scientific analysis platform — Direct categorical statement. — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | concept_edges[0] |
| relation | Galaxy —is-a→ web-based scientific analysis platform — Galaxy is a web-based scientific analysis platform. — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | concept_edges[0] |
| note | Galaxy —contrasted-with→ earlier versions of Galaxy — Explicit whereas comparison. — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | concept_edges[1] |
| relation | Galaxy —contrasted-with→ earlier versions of Galaxy — The current Galaxy ran the many-item experiment seamlessly, whereas earlier versions could not support it. — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | concept_edges[1] |
| weight | Galaxy —contrasted-with→ earlier versions of Galaxy — 0.99 — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | concept_edges[1] |
| note | named-tag —part-of→ provenance system — Explicit part-of statement. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | concept_edges[2] |
| relation | named-tag —part-of→ provenance system — Named-tags become part of the Galaxy provenance system. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | concept_edges[2] |
| note | Galaxy —same-as→ Galaxy Project — The introduction uses the names for the same project context. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concept_edges[3] |
| relation | Galaxy —same-as→ Galaxy Project — Galaxy and the Galaxy Project name the platform and surrounding project discussed in the article. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concept_edges[3] |
| weight | Galaxy —same-as→ Galaxy Project — 0.95 — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concept_edges[3] |
| audit | client-side code —causes→ response time — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but client-side code IMPROVES (shortens) response time; the edge lacks its direction. — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concept_edges[5] |
| note | client-side code —causes→ response time — Flat positive effect with a stated mechanism. — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concept_edges[5] |
| relation | client-side code —causes→ response time — Client-side code improves response time by enabling partial page updates. — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concept_edges[5] |
| weight | client-side code —causes→ response time — 0.95 — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concept_edges[5] |
| evidence status note | concept — THE QUOTE ENDS ON A COLON: the paper goes on with the four components of the Galaxy Project, one paragraph each, too long to quote here; what this row states is in that list, not in the quoted words. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[7] |
| evidence status note | concept — THE QUOTE ENDS ON A COLON: the paper goes on with the four components of the Galaxy Project, one paragraph each, too long to quote here; what this row states is in that list, not in the quoted words. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[8] |
| evidence status note | sense — THE QUOTE ENDS ON A COLON: the paper goes on with the four components of the Galaxy Project, one paragraph each, too long to quote here; what this row states is in that list, not in the quoted words. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | senses[1] |
| evidence status note | Galaxy Project → four complementary components — THE QUOTE ENDS ON A COLON: the paper goes on with the four components of the Galaxy Project, one paragraph each, too long to quote here; what this row states is in that list, not in the quoted words. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[0] |
| relation | Galaxy Project → four complementary components — The Galaxy Project consists of four complementary components. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[0] |
| relation | named-tag → tags — A hash-prefixed tag is referred to as a named-tag. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | unhoused_relations[1] |
| relation | Galaxy → data-driven biomedical science — Galaxy enables data-driven biomedical science through accessible, reproducible, transparent analysis. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[2] |
| weight | Galaxy → data-driven biomedical science — 0.95 — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[2] |
| relation | Galaxy flavors → Galaxy-Docker — Galaxy-Docker delivers personalized portable instances known as Galaxy flavors. — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | unhoused_relations[3] |
| weight | Galaxy flavors → Galaxy-Docker — 0.99 — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | unhoused_relations[3] |
| relation | Biostars → Galaxy Community — The Galaxy user community provides many answers through the monitored Biostars forum. — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[4] |
| weight | Biostars → Galaxy Community — 0.95 — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[4] |
| relation | RESTful Galaxy API → Galaxy API — The client-side RESTful Galaxy API and the Webhook-facing Galaxy API refer to the platform's programmatic interface. — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | unhoused_relations[5] |
| weight | RESTful Galaxy API → Galaxy API — 0.9 — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | unhoused_relations[5] |
| relation | Galaxy framework and software ecosystem → software development kit — The Galaxy ecosystem includes a software development kit for tool development. — “The Galaxy framework and software ecosystem ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxy server on any Unix-based operating system. The Galaxy ecosystem includes a software development kit (SDK) for Galaxy tool development, API language bindings for multiple programming languages, software for scripting Galaxy interactions, and tools for automating setup and deployment of Galaxy and its plugins such as tools and visualizations.” | unhoused_relations[6] |
| relation | Galaxy ToolShed → AppStore — The Galaxy ToolShed assumes the role of an AppStore for Galaxy instances. — “The Galaxy ToolShed ( 11 ) assumes the role of an AppStore for Galaxy instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxy servers and computing environments.” | unhoused_relations[7] |
| weight | Galaxy ToolShed → AppStore — 0.95 — “The Galaxy ToolShed ( 11 ) assumes the role of an AppStore for Galaxy instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxy servers and computing environments.” | unhoused_relations[7] |
| relation | scalability → large biomedical datasets — Growing biomedical dataset size and number create a scalability challenge for Galaxy. — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | unhoused_relations[8] |
| weight | scalability → large biomedical datasets — 0.99 — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | unhoused_relations[8] |
| relation | workflows → User interface scalability — Workflows and collections facilitate analysis of large numbers of datasets. — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | unhoused_relations[9] |
| weight | workflows → User interface scalability — 0.95 — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | unhoused_relations[9] |
| relation | dynamic job destination assignment → Server scalability — Dynamic job destination assignment chooses resources based on job parameters. — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | unhoused_relations[10] |
| weight | dynamic job destination assignment → Server scalability — 0.99 — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | unhoused_relations[10] |
| relation | XSEDE infrastructure → jobs — Specialized national resources enable Galaxy Main to execute larger jobs. — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[11] |
| weight | XSEDE infrastructure → jobs — 0.95 — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[11] |
| relation | Jetstream cloud → Galaxy Interactive Environments — Jetstream virtualization enables isolated interactive environments. — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[12] |
| weight | Jetstream cloud → Galaxy Interactive Environments — 0.95 — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[12] |
| relation | CloudBridge → CloudLaunch — CloudBridge and CloudLaunch jointly launch Galaxy across providers while reducing provider-specific maintenance. — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | unhoused_relations[13] |
| weight | CloudBridge → CloudLaunch — 0.95 — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | unhoused_relations[13] |
| relation | tool profile version → earlier versions of Galaxy — Tool profile versions preserve backward compatibility while defaults and behavior evolve. — “The portability and backward-compatibility of the Galaxy tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile version , which is used to ensure compatibility between a version of a tool and its targeted Galaxy version. In addition, tool profile versions allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflow. We improved the installation process so that restarting Galaxy is not required to use a newly installed tool.” | unhoused_relations[14] |
| weight | tool profile version → earlier versions of Galaxy — 0.99 — “The portability and backward-compatibility of the Galaxy tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile version , which is used to ensure compatibility between a version of a tool and its targeted Galaxy version. In addition, tool profile versions allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflow. We improved the installation process so that restarting Galaxy is not required to use a newly installed tool.” | unhoused_relations[14] |
| relation | Galaxy Interactive Environments → Jupyter — Interactive Environments integrate Galaxy with Jupyter. — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | unhoused_relations[15] |
| weight | Galaxy Interactive Environments → Jupyter — 0.99 — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | unhoused_relations[15] |
| relation | exploratory → pipeline execution — Users can switch seamlessly between exploratory analysis and workflow execution. — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | unhoused_relations[16] |
| weight | exploratory → pipeline execution — 0.99 — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | unhoused_relations[16] |
| relation | named-tag → workflows — Named-tags maintain dataset identifiers throughout large workflows. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | unhoused_relations[17] |
| weight | named-tag → workflows — 0.99 — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | unhoused_relations[17] |
| relation | Webhooks → Galaxy API — Webhooks use the Galaxy API to trigger server-side functions from frontend extensions. — “Inspired by user feedback and the need to quickly modify and adapt Galaxy's interface, we integrated a pluggable system to extend Galaxy's frontend. Webhooks provide an entry-point into the Galaxy UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side code (JavaScript, HTML, CSS) and interact with the rest of the Galaxy user-interface. By integrating Webhooks with the Galaxy API, it is also possible to trigger server-side functions from within a Webhook. Webhooks can be thoroughly customized and are enabled at the discretion of the Galaxy administrator.” | unhoused_relations[18] |
| weight | Webhooks → Galaxy API — 0.99 — “Inspired by user feedback and the need to quickly modify and adapt Galaxy's interface, we integrated a pluggable system to extend Galaxy's frontend. Webhooks provide an entry-point into the Galaxy UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side code (JavaScript, HTML, CSS) and interact with the rest of the Galaxy user-interface. By integrating Webhooks with the Galaxy API, it is also possible to trigger server-side functions from within a Webhook. Webhooks can be thoroughly customized and are enabled at the discretion of the Galaxy administrator.” | unhoused_relations[18] |
| relation | Tour Builder → interactive tours — Tour Builder records, replays, updates, and exports interactive tours. — “We have developed self-paced, interactive tours that users can step through to learn about Galaxy. These tours guide users step by step through using the interface including tools, workflows, and other features available in Galaxy. To simplify tour creation, a Tour Builder ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.” | unhoused_relations[19] |
| weight | Tour Builder → interactive tours — 0.99 — “We have developed self-paced, interactive tours that users can step through to learn about Galaxy. These tours guide users step by step through using the interface including tools, workflows, and other features available in Galaxy. To simplify tour creation, a Tour Builder ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.” | unhoused_relations[19] |
| relation | Galaxy Dataset Collections → collections — Galaxy Dataset Collections organize potentially nested sets for simultaneous analysis. — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | unhoused_relations[20] |
| weight | Galaxy Dataset Collections → collections — 0.99 — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | unhoused_relations[20] |
| relation | uWSGI → scalability — uWSGI improves production scalability, load balancing, fault tolerance, and uninterrupted restarts. — “In order to make Galaxy more robust in a production environment, we adopted technologies to enhance Galaxy's portability, security, reliability, and scalability. Galaxy now utilizes uWSGI ( http://projects.unbit.it/uwsgi ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalability, improved fault tolerance and the possibility of restarting Galaxy uninterruptedly.” | unhoused_relations[21] |
| weight | uWSGI → scalability — 0.95 — “In order to make Galaxy more robust in a production environment, we adopted technologies to enhance Galaxy's portability, security, reliability, and scalability. Galaxy now utilizes uWSGI ( http://projects.unbit.it/uwsgi ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalability, improved fault tolerance and the possibility of restarting Galaxy uninterruptedly.” | unhoused_relations[21] |
| relation | CernVM File System → Galaxy Main — CVMFS shares versioned reference data from Galaxy Main with other instances. — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | unhoused_relations[22] |
| weight | CernVM File System → Galaxy Main — 0.99 — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | unhoused_relations[22] |
| relation | Conda package manager → reproducible tool execution environments — Conda resolves dependencies used in reproducible Galaxy tool environments. — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[23] |
| weight | Conda package manager → reproducible tool execution environments — 0.99 — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[23] |
| relation | Docker → reproducible tool execution environments — Docker provides isolated portable tool environments. — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[24] |
| weight | Docker → reproducible tool execution environments — 0.95 — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[24] |
| relation | Ansible → Galaxy Main — Ansible configurations automate production deployment of Galaxy instances. — “Galaxy is a generic data analysis framework, which can be configured for various application scenarios using a wide range of configuration parameters. To facilitate configuring these parameters with optimal values for a number of predefined application scenarios, the Galaxy project leverages Ansible ( https://www.ansible.com ), software for automated configuration and management of other software packages. We have developed and shared Ansible configurations for Galaxy Main , the main public Galaxy server, ( https://github.com/galaxyproject/usegalaxy-playbook ) and also a configurable generic playbook for setting up production instances on cloud resources, virtual machines, and bare metal ( https://github.com/ARTbio/GalaxyKickStart ). This playbook can be used as a reference for configuring a Galaxy instance for a production environment.” | unhoused_relations[25] |
| weight | Ansible → Galaxy Main — 0.99 — “Galaxy is a generic data analysis framework, which can be configured for various application scenarios using a wide range of configuration parameters. To facilitate configuring these parameters with optimal values for a number of predefined application scenarios, the Galaxy project leverages Ansible ( https://www.ansible.com ), software for automated configuration and management of other software packages. We have developed and shared Ansible configurations for Galaxy Main , the main public Galaxy server, ( https://github.com/galaxyproject/usegalaxy-playbook ) and also a configurable generic playbook for setting up production instances on cloud resources, virtual machines, and bare metal ( https://github.com/ARTbio/GalaxyKickStart ). This playbook can be used as a reference for configuring a Galaxy instance for a production environment.” | unhoused_relations[25] |
| relation | Sentry → continuous integration — Sentry tracks runtime errors while continuous integration tests proposed code changes. — “Server monitoring and issue management is crucial in production Galaxy instances. Galaxy has integrated a plugin module to submit user bug-reports to configurable endpoints such as mailing lists or GitHub issues. With this, Galaxy can be configured to send error reports to a local ticket system. The recent integration of Sentry ( https://sentry.io/ ) for automated error tracking and reporting makes it easier for administrators to track both client- and server-side errors without requiring manual user bug reports.” | unhoused_relations[26] |
| weight | Sentry → continuous integration — 0.9 — “Server monitoring and issue management is crucial in production Galaxy instances. Galaxy has integrated a plugin module to submit user bug-reports to configurable endpoints such as mailing lists or GitHub issues. With this, Galaxy can be configured to send error reports to a local ticket system. The recent integration of Sentry ( https://sentry.io/ ) for automated error tracking and reporting makes it easier for administrators to track both client- and server-side errors without requiring manual user bug reports.” | unhoused_relations[26] |
| relation | Markdown → Galaxy Community Hub — Markdown pull requests are reviewed and regenerate the static Community Hub. — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | unhoused_relations[27] |
| weight | Markdown → Galaxy Community Hub — 0.99 — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | unhoused_relations[27] |
| relation | Biostars → user community — The user community supplies many answers on the monitored Biostars forum. — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[28] |
| weight | Biostars → user community — 0.95 — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[28] |
| relation | Planemo → Galaxy ToolShed — Planemo tests, installs, and uploads community tools to the ToolShed. — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | unhoused_relations[29] |
| weight | Planemo → Galaxy ToolShed — 0.99 — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | unhoused_relations[29] |
| relation | continuous integration → codebase — Continuous integration helps prevent bugs from entering the codebase. — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | unhoused_relations[30] |
| weight | continuous integration → codebase — 0.95 — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | unhoused_relations[30] |
| no type for this | Galaxy Project → four complementary components — The Galaxy Project consists of four complementary components. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[0] |
| no type for this | named-tag → tags — A hash-prefixed tag is referred to as a named-tag. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | unhoused_relations[1] |
| no type for this | Galaxy → data-driven biomedical science — Galaxy enables data-driven biomedical science through accessible, reproducible, transparent analysis. — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | unhoused_relations[2] |
| no type for this | Galaxy flavors → Galaxy-Docker — Galaxy-Docker delivers personalized portable instances known as Galaxy flavors. — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | unhoused_relations[3] |
| no type for this | Biostars → Galaxy Community — The Galaxy user community provides many answers through the monitored Biostars forum. — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[4] |
| no type for this | RESTful Galaxy API → Galaxy API — The client-side RESTful Galaxy API and the Webhook-facing Galaxy API refer to the platform's programmatic interface. — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | unhoused_relations[5] |
| no type for this | Galaxy framework and software ecosystem → software development kit — The Galaxy ecosystem includes a software development kit for tool development. — “The Galaxy framework and software ecosystem ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxy server on any Unix-based operating system. The Galaxy ecosystem includes a software development kit (SDK) for Galaxy tool development, API language bindings for multiple programming languages, software for scripting Galaxy interactions, and tools for automating setup and deployment of Galaxy and its plugins such as tools and visualizations.” | unhoused_relations[6] |
| no type for this | Galaxy ToolShed → AppStore — The Galaxy ToolShed assumes the role of an AppStore for Galaxy instances. — “The Galaxy ToolShed ( 11 ) assumes the role of an AppStore for Galaxy instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxy servers and computing environments.” | unhoused_relations[7] |
| no type for this | scalability → large biomedical datasets — Growing biomedical dataset size and number create a scalability challenge for Galaxy. — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | unhoused_relations[8] |
| no type for this | workflows → User interface scalability — Workflows and collections facilitate analysis of large numbers of datasets. — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | unhoused_relations[9] |
| no type for this | dynamic job destination assignment → Server scalability — Dynamic job destination assignment chooses resources based on job parameters. — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | unhoused_relations[10] |
| no type for this | XSEDE infrastructure → jobs — Specialized national resources enable Galaxy Main to execute larger jobs. — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[11] |
| no type for this | Jetstream cloud → Galaxy Interactive Environments — Jetstream virtualization enables isolated interactive environments. — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | unhoused_relations[12] |
| no type for this | CloudBridge → CloudLaunch — CloudBridge and CloudLaunch jointly launch Galaxy across providers while reducing provider-specific maintenance. — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | unhoused_relations[13] |
| no type for this | tool profile version → earlier versions of Galaxy — Tool profile versions preserve backward compatibility while defaults and behavior evolve. — “The portability and backward-compatibility of the Galaxy tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile version , which is used to ensure compatibility between a version of a tool and its targeted Galaxy version. In addition, tool profile versions allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflow. We improved the installation process so that restarting Galaxy is not required to use a newly installed tool.” | unhoused_relations[14] |
| no type for this | Galaxy Interactive Environments → Jupyter — Interactive Environments integrate Galaxy with Jupyter. — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | unhoused_relations[15] |
| no type for this | exploratory → pipeline execution — Users can switch seamlessly between exploratory analysis and workflow execution. — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | unhoused_relations[16] |
| no type for this | named-tag → workflows — Named-tags maintain dataset identifiers throughout large workflows. — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | unhoused_relations[17] |
| no type for this | Webhooks → Galaxy API — Webhooks use the Galaxy API to trigger server-side functions from frontend extensions. — “Inspired by user feedback and the need to quickly modify and adapt Galaxy's interface, we integrated a pluggable system to extend Galaxy's frontend. Webhooks provide an entry-point into the Galaxy UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side code (JavaScript, HTML, CSS) and interact with the rest of the Galaxy user-interface. By integrating Webhooks with the Galaxy API, it is also possible to trigger server-side functions from within a Webhook. Webhooks can be thoroughly customized and are enabled at the discretion of the Galaxy administrator.” | unhoused_relations[18] |
| no type for this | Tour Builder → interactive tours — Tour Builder records, replays, updates, and exports interactive tours. — “We have developed self-paced, interactive tours that users can step through to learn about Galaxy. These tours guide users step by step through using the interface including tools, workflows, and other features available in Galaxy. To simplify tour creation, a Tour Builder ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.” | unhoused_relations[19] |
| no type for this | Galaxy Dataset Collections → collections — Galaxy Dataset Collections organize potentially nested sets for simultaneous analysis. — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | unhoused_relations[20] |
| no type for this | uWSGI → scalability — uWSGI improves production scalability, load balancing, fault tolerance, and uninterrupted restarts. — “In order to make Galaxy more robust in a production environment, we adopted technologies to enhance Galaxy's portability, security, reliability, and scalability. Galaxy now utilizes uWSGI ( http://projects.unbit.it/uwsgi ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalability, improved fault tolerance and the possibility of restarting Galaxy uninterruptedly.” | unhoused_relations[21] |
| no type for this | CernVM File System → Galaxy Main — CVMFS shares versioned reference data from Galaxy Main with other instances. — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | unhoused_relations[22] |
| no type for this | Conda package manager → reproducible tool execution environments — Conda resolves dependencies used in reproducible Galaxy tool environments. — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[23] |
| no type for this | Docker → reproducible tool execution environments — Docker provides isolated portable tool environments. — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | unhoused_relations[24] |
| no type for this | Ansible → Galaxy Main — Ansible configurations automate production deployment of Galaxy instances. — “Galaxy is a generic data analysis framework, which can be configured for various application scenarios using a wide range of configuration parameters. To facilitate configuring these parameters with optimal values for a number of predefined application scenarios, the Galaxy project leverages Ansible ( https://www.ansible.com ), software for automated configuration and management of other software packages. We have developed and shared Ansible configurations for Galaxy Main , the main public Galaxy server, ( https://github.com/galaxyproject/usegalaxy-playbook ) and also a configurable generic playbook for setting up production instances on cloud resources, virtual machines, and bare metal ( https://github.com/ARTbio/GalaxyKickStart ). This playbook can be used as a reference for configuring a Galaxy instance for a production environment.” | unhoused_relations[25] |
| no type for this | Sentry → continuous integration — Sentry tracks runtime errors while continuous integration tests proposed code changes. — “Server monitoring and issue management is crucial in production Galaxy instances. Galaxy has integrated a plugin module to submit user bug-reports to configurable endpoints such as mailing lists or GitHub issues. With this, Galaxy can be configured to send error reports to a local ticket system. The recent integration of Sentry ( https://sentry.io/ ) for automated error tracking and reporting makes it easier for administrators to track both client- and server-side errors without requiring manual user bug reports.” | unhoused_relations[26] |
| no type for this | Markdown → Galaxy Community Hub — Markdown pull requests are reviewed and regenerate the static Community Hub. — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | unhoused_relations[27] |
| no type for this | Biostars → user community — The user community supplies many answers on the monitored Biostars forum. — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | unhoused_relations[28] |
| no type for this | Planemo → Galaxy ToolShed — Planemo tests, installs, and uploads community tools to the ToolShed. — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | unhoused_relations[29] |
| no type for this | continuous integration → codebase — Continuous integration helps prevent bugs from entering the codebase. — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | unhoused_relations[30] |
| limitation | Galaxy must scale both its browser interface and backend server under growing dataset size, dataset count, and multiuser demand — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | limitations[0] |
| limitation | earlier Galaxy versions could not support the demonstrated 11,872-history-item single-cell analysis — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | limitations[1] |
| limitation | interactive environments require execution isolation because arbitrary scripting raises security concerns — “Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Main enhances user-experience. It is now possible to run jobs that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobs, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloud. Consequently, it is possible to concurrently run more jobs due to the increase in processing capacity.” | limitations[2] |
| limitation | cloud deployments otherwise require provider-specific machine images and file systems to be built and maintained — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | limitations[3] |
| limitation | complete genomic analyses often still require custom scripts or visualizations at preparation or summarization stages — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | limitations[4] |
| limitation | input-output connections become less obvious in large workflows without persistent tag propagation — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | limitations[5] |
| limitation | workflow versioning, nesting, efficient background scheduling, and external-format support were recent or initial capabilities — “Galaxy workflows have been extended in several ways. Switching between tool versions and upgrading workflows with new tool versions is now supported. A workflow can now be embedded in another, making it easier to create and edit workflows that have many common steps repeated. Many of these features have existed in in standalone workflow systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxy users. Workflows are now scheduled by a Galaxy server more efficiently and in the background, making it possible to execute larger workflows, generating tens of thousands of jobs, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxy with initial support for running workflows defined in the Common Workflow Language ( 32 ) format.” | limitations[6] |
| limitation | manually installing and configuring reference data on every Galaxy server created time-intensive redundant effort — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | limitations[7] |
| limitation | production instances need explicit portability, security, reliability, scalability, monitoring, and issue-management infrastructure — “In order to make Galaxy more robust in a production environment, we adopted technologies to enhance Galaxy's portability, security, reliability, and scalability. Galaxy now utilizes uWSGI ( http://projects.unbit.it/uwsgi ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalability, improved fault tolerance and the possibility of restarting Galaxy uninterruptedly.” | limitations[8] |
| limitation | core future work remained in federation, provenance coupling, ToolShed development, collections, workflows, interfaces, training, and instrumentation — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | limitations[9] |
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
| concept | Galaxy — not defined in this paper's own words; a web-based scientific platform for accessible, reproducible, shareable biomedical analysis — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | concepts[0] |
| concept | web-based scientific analysis platform — not defined in this paper's own words; the system class to which Galaxy belongs — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | concepts[1] |
| concept | large biomedical datasets — not defined in this paper's own words; genomics, proteomics, metabolomics, and imaging data analyzed through Galaxy — “Galaxy (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.” | concepts[2] |
| concept | data-driven biomedical science — not defined in this paper's own words; biomedical inquiry grounded in large-scale data analysis — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[3] |
| concept | accessible data analysis — not defined in this paper's own words; analysis usable regardless of informatics expertise — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[4] |
| concept | reproducible analyses — not defined in this paper's own words; analyses that can be replayed regardless of platform — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[5] |
| concept | transparent communication — not defined in this paper's own words; communication that supports reuse and extension across communities — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[6] |
| concept | Galaxy Project — not defined in this paper's own words; the server, framework, ToolShed, and community considered together — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[7] |
| concept | four complementary components — not defined in this paper's own words; the major project components enumerated by the paper — “Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Project ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical science by pursuing three goals: (a) accessible data analysis serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analyses regardless of the particular platform and (c) transparent communication of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Project consists of four complementary components:” | concepts[8] |
| concept | main public Galaxy server — not defined in this paper's own words; the public analysis service at usegalaxy.org — “The main public Galaxy server ( https://usegalaxy.org )—this server is the subject of this article and has been online since 2007. It features a rich toolset for large-scale genomics analyses, terabytes of public data for use, and hundreds of shared analysis histories, workflows, and interactive publication supplements. This server has more than 124,000 registered users whom run ∼245,000 analysis jobs each month.” | concepts[9] |
| concept | Galaxy framework and software ecosystem — not defined in this paper's own words; the open-source server package, SDK, bindings, scripts, and deployment tools — “The Galaxy framework and software ecosystem ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxy server on any Unix-based operating system. The Galaxy ecosystem includes a software development kit (SDK) for Galaxy tool development, API language bindings for multiple programming languages, software for scripting Galaxy interactions, and tools for automating setup and deployment of Galaxy and its plugins such as tools and visualizations.” | concepts[10] |
| concept | software development kit — not defined in this paper's own words; the SDK supporting Galaxy tool development — “The Galaxy framework and software ecosystem ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxy server on any Unix-based operating system. The Galaxy ecosystem includes a software development kit (SDK) for Galaxy tool development, API language bindings for multiple programming languages, software for scripting Galaxy interactions, and tools for automating setup and deployment of Galaxy and its plugins such as tools and visualizations.” | concepts[11] |
| concept | API language bindings — not defined in this paper's own words; programming-language interfaces to Galaxy services — “The Galaxy framework and software ecosystem ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxy server on any Unix-based operating system. The Galaxy ecosystem includes a software development kit (SDK) for Galaxy tool development, API language bindings for multiple programming languages, software for scripting Galaxy interactions, and tools for automating setup and deployment of Galaxy and its plugins such as tools and visualizations.” | concepts[12] |
| concept | Galaxy ToolShed — not defined in this paper's own words; the community repository for tools, workflows, and visualizations — “The Galaxy ToolShed ( https://toolshed.g2.bx.psu.edu/ )—a community-driven resource for the dissemination of Galaxy tools, workflows, and visualizations. This server functions as an ‘AppStore’ for Galaxy servers where developers and Galaxy admins can host, share, and install Galaxy tools, workflows and visualizations.” | concepts[13] |
| concept | AppStore — not defined in this paper's own words; the distribution role played by the ToolShed for Galaxy instances — “The Galaxy ToolShed ( 11 ) assumes the role of an AppStore for Galaxy instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxy servers and computing environments.” | concepts[14] |
| concept | Galaxy Community — not defined in this paper's own words; complementary stakeholder subcommunities contributing to the project — “The Galaxy Community ( https://galaxyproject.org/community/ )—distinct and complementary subcommunities make key contributions to all aspects of the Project. These subcommunities address the needs and desires of every category of stakeholder including users, administrators, developers, resource providers and educators.” | concepts[15] |
| concept | user community — not defined in this paper's own words; the users who answer questions, test the platform, and contribute improvements — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | concepts[16] |
| concept | scalability — not defined in this paper's own words; the capacity of Galaxy's interface and backend to handle growing datasets, tasks, and users — “Scalability is amongst the most significant challenges that Galaxy faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxy must scale both in its web-based interface and on its backend server and do so in a multiuser environment.” | concepts[17] |
| concept | User interface scalability — not defined in this paper's own words; the capacity to manipulate many datasets and build collective pipelines through the web interface — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | concepts[18] |
| concept | Server scalability — not defined in this paper's own words; the capacity to execute many tasks for many users across heterogeneous compute resources — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[19] |
| concept | earlier versions of Galaxy — not defined in this paper's own words; prior releases unable to support the demonstrated many-item analysis — “User interface scalability enables scientists to use the Galaxy web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxy implements a variety of features to facilitate analyzing large numbers of datasets, including workflows and collections . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalability. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxy ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxy would not have been able to support this analysis.” | concepts[20] |
| concept | workflows — not defined in this paper's own words; reusable executable pipelines generated interactively or edited directly — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | concepts[21] |
| concept | collections — not defined in this paper's own words; possibly nested groups of datasets supporting batch operations — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | concepts[22] |
| concept | jobs — not defined in this paper's own words; atomic units of work dispatched by Galaxy — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[23] |
| concept | workload managers — not defined in this paper's own words; systems such as Slurm, HTCondor, Mesos, and Kubernetes used to dispatch jobs — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[24] |
| concept | dynamic job destination assignment — not defined in this paper's own words; automatic resource selection based on job parameters — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[25] |
| concept | Galaxy Main — not defined in this paper's own words; the main public deployment that uses national and cloud compute resources — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[26] |
| concept | XSEDE infrastructure — not defined in this paper's own words; national cyberinfrastructure including Bridges and Stampede — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[27] |
| concept | Jetstream cloud — not defined in this paper's own words; cloud infrastructure used for isolated and interactive workloads — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[28] |
| concept | containerization technologies — not defined in this paper's own words; isolation mechanisms enabling interactive jobs on shared infrastructure — “Server scalability refers to the Galaxy's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxy framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxy is highly versatile in its ability to deploy jobs (atomic units of work), as it can leverage a multitude of workload managers including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight job running system. Recent enhancements to Galaxy's job management include dynamic job destination assignment (which facilitate automatic job parameter-specific resource selection), delay in job queuing (e.g. for workflows), automatic job re-submission (e.g., on job failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Main (Figure 2 ) to leverage cloud computing resources for better job throughput. Specifically, Galaxy Main is now configured to take advantage of the XSEDE infrastructure ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloud ( 9 ). The benefits of using these resources include the ability to run larger jobs, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environments through the ability to use containerization technologies and provide sufficient isolation of individual jobs from other processes running on the same underlying compute infrastructure.” | concepts[29] |
| concept | Galaxy Interactive Environments — not defined in this paper's own words; integrations of Galaxy infrastructure with interactive scripting systems — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | concepts[30] |
| concept | CloudBridge — not defined in this paper's own words; a cloud abstraction library reducing provider-specific implementation — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | concepts[31] |
| concept | CloudLaunch — not defined in this paper's own words; an application for launching Galaxy across cloud providers — “A complete Galaxy server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridge ( 10 ) and a new CloudLaunch application. These two solutions make it possible to launch Galaxy instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxy available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).” | concepts[32] |
| concept | tool profile version — not defined in this paper's own words; a configuration value matching tool behavior to a targeted Galaxy version — “The portability and backward-compatibility of the Galaxy tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile version , which is used to ensure compatibility between a version of a tool and its targeted Galaxy version. In addition, tool profile versions allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflow. We improved the installation process so that restarting Galaxy is not required to use a newly installed tool.” | concepts[33] |
| concept | Galaxy API — not defined in this paper's own words; the programming interface for client, tool, workflow, and Webhook operations — “Inspired by user feedback and the need to quickly modify and adapt Galaxy's interface, we integrated a pluggable system to extend Galaxy's frontend. Webhooks provide an entry-point into the Galaxy UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side code (JavaScript, HTML, CSS) and interact with the rest of the Galaxy user-interface. By integrating Webhooks with the Galaxy API, it is also possible to trigger server-side functions from within a Webhook. Webhooks can be thoroughly customized and are enabled at the discretion of the Galaxy administrator.” | concepts[34] |
| concept | Jupyter — not defined in this paper's own words; an interactive scripting platform integrated with Galaxy — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | concepts[35] |
| concept | RStudio — not defined in this paper's own words; an interactive scripting environment described as under development for integration — “Galaxy's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environments ( 26 ), an integration of Galaxy with Jupyter (RStudio is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxy users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.” | concepts[36] |
| concept | visualization framework — not defined in this paper's own words; the framework integrating web and server-side biological visualizations — “Galaxy's visualization framework ( 27 ) makes it possible to integrate a wide variety of Web-based and server-side visualizations. Through this framework, many new visualizations have been added to Galaxy, including Cytoscape ( 28 ), and the WebGL enabled 3D Protein viewer NGL ( 29 ), molecular interaction networks and macromolecular structures visualizations, and the 100+ visualizations available through BioJS ( 30 ), a rich set of community-driven JavaScript components for agile and interactive visualization of biological data.” | concepts[37] |
| concept | exploratory — not defined in this paper's own words; the trial-and-error interactive mode of data analysis — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | concepts[38] |
| concept | pipeline execution — not defined in this paper's own words; the automated workflow mode of data analysis — “There are two common modes of data analysis: exploratory and pipeline execution. Galaxy enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflows from an ad hoc analysis. An interactive workflow editor is also available to modify or generate workflows from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflow on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflows, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.” | concepts[39] |
| concept | RESTful Galaxy API — not defined in this paper's own words; the interface connecting client-side code to the backend — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concepts[40] |
| concept | client-side code — not defined in this paper's own words; browser-resident interface code communicating with the backend through the RESTful API — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concepts[41] |
| concept | response time — not defined in this paper's own words; interface latency improved by partial rather than full-page updates — “The client-side of Galaxy, which is the user-interface most people associate with Galaxy, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy API with the backend. This minimizes the number of full-page refreshes and improves response time by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.” | concepts[42] |
| concept | Vue.js — not defined in this paper's own words; the reactive framework selected for future client-side interface development — “Furthermore, the community has selected the Vue.js framework ( https://vuejs.org/ ) as the base for future improvements allowing all UI elements to converge into a more reactive and future-proof interface. With the integration of Vue.js, the entire client-side build system was updated to utilize the latest web-technologies, to make routing and loading times faster, and to encourage rapid future interface improvements. While mostly transparent to users, these changes are the fundamental groundwork of a much more flexible UI framework that will enable visual enhancements and an improved user experience for years to come.” | concepts[43] |
| concept | tags — not defined in this paper's own words; dataset labels propagated through analysis steps — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | concepts[44] |
| concept | named-tag — not defined in this paper's own words; a hash-prefixed tag maintained through an analysis workflow — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | concepts[45] |
| concept | provenance system — not defined in this paper's own words; Galaxy's tracking structure for datasets across their analysis lifecycle — “Although tags have been supported in Galaxy for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tags to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance system and ease-of-use of Galaxy. To enable automatic tag propagation, a hash-sign (#) is placed at the beginning of the tag, which is colloquially referred to as a named-tag. While standard Galaxy output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflows becomes increasingly less obvious; by utilizing named-tags, users can label datasets with an identifier that is maintained throughout the analysis.” | concepts[46] |
| concept | Webhooks — not defined in this paper's own words; pluggable frontend entry points for client and server-side extensions — “Inspired by user feedback and the need to quickly modify and adapt Galaxy's interface, we integrated a pluggable system to extend Galaxy's frontend. Webhooks provide an entry-point into the Galaxy UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side code (JavaScript, HTML, CSS) and interact with the rest of the Galaxy user-interface. By integrating Webhooks with the Galaxy API, it is also possible to trigger server-side functions from within a Webhook. Webhooks can be thoroughly customized and are enabled at the discretion of the Galaxy administrator.” | concepts[47] |
| concept | interactive tours — not defined in this paper's own words; self-paced stepwise interface training — “We have developed self-paced, interactive tours that users can step through to learn about Galaxy. These tours guide users step by step through using the interface including tools, workflows, and other features available in Galaxy. To simplify tour creation, a Tour Builder ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.” | concepts[48] |
| concept | Tour Builder — not defined in this paper's own words; a tool for recording, replaying, updating, and exporting tours — “We have developed self-paced, interactive tours that users can step through to learn about Galaxy. These tours guide users step by step through using the interface including tools, workflows, and other features available in Galaxy. To simplify tour creation, a Tour Builder ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.” | concepts[49] |
| concept | Common Workflow Language — not defined in this paper's own words; an external workflow format initially supported by Galaxy — “Galaxy workflows have been extended in several ways. Switching between tool versions and upgrading workflows with new tool versions is now supported. A workflow can now be embedded in another, making it easier to create and edit workflows that have many common steps repeated. Many of these features have existed in in standalone workflow systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxy users. Workflows are now scheduled by a Galaxy server more efficiently and in the background, making it possible to execute larger workflows, generating tens of thousands of jobs, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxy with initial support for running workflows defined in the Common Workflow Language ( 32 ) format.” | concepts[50] |
| concept | Galaxy Dataset Collections — not defined in this paper's own words; nested dataset groups supporting simultaneous analysis and batch tools — “Galaxy Dataset Collections combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collections together, we recently introduced specialized tools to be executed on collections (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collections and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collections to and from both user's local disk and Galaxy data libraries.” | concepts[51] |
| concept | uWSGI — not defined in this paper's own words; the default web application server adopted for production robustness — “In order to make Galaxy more robust in a production environment, we adopted technologies to enhance Galaxy's portability, security, reliability, and scalability. Galaxy now utilizes uWSGI ( http://projects.unbit.it/uwsgi ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalability, improved fault tolerance and the possibility of restarting Galaxy uninterruptedly.” | concepts[52] |
| concept | CernVM File System — not defined in this paper's own words; a scalable content-addressable file system for shared versioned reference data — “Many tools available via Galaxy rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxy is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxy administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxy server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Main available via a CernVM File System (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxy, including Galaxy-Docker images, can attach to this file system and gain access to the same reference data available on Galaxy Main. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.” | concepts[53] |
| concept | Conda package manager — not defined in this paper's own words; Galaxy's default resolver for tool dependencies — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[54] |
| concept | Docker — not defined in this paper's own words; a containerization technology supported for portable isolated execution — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[55] |
| concept | Singularity — not defined in this paper's own words; a containerization technology supported for portable isolated execution — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[56] |
| concept | Bioconda — not defined in this paper's own words; an open project leveraged for reproducible tool environments — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[57] |
| concept | BioContainer — not defined in this paper's own words; an open project leveraged for reproducible containerized tool environments — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[58] |
| concept | reproducible tool execution environments — not defined in this paper's own words; portable dependency-controlled environments provisioned by Galaxy — “Galaxy is powered by various open-source projects which are installed automatically, and used when needed. Galaxy is using the Conda package manager ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologies (e.g. Docker ( https://www.docker.com ) and Singularity ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Bioconda ( https://doi.org/10.1101/207092 ) and the BioContainer ( 36 ) projects, Galaxy is able to provision and use reproducible tool execution environments (( 37 ); https://doi.org/10.1101/200683 ).” | concepts[59] |
| concept | Ansible — not defined in this paper's own words; software for automated configuration and management of Galaxy deployments — “Galaxy is a generic data analysis framework, which can be configured for various application scenarios using a wide range of configuration parameters. To facilitate configuring these parameters with optimal values for a number of predefined application scenarios, the Galaxy project leverages Ansible ( https://www.ansible.com ), software for automated configuration and management of other software packages. We have developed and shared Ansible configurations for Galaxy Main , the main public Galaxy server, ( https://github.com/galaxyproject/usegalaxy-playbook ) and also a configurable generic playbook for setting up production instances on cloud resources, virtual machines, and bare metal ( https://github.com/ARTbio/GalaxyKickStart ). This playbook can be used as a reference for configuring a Galaxy instance for a production environment.” | concepts[60] |
| concept | Galaxy-Docker — not defined in this paper's own words; a project delivering production-ready portable Galaxy instances — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | concepts[61] |
| concept | Galaxy flavors — not defined in this paper's own words; personalized self-contained portable Galaxy instances — “The Galaxy-Docker project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxy instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxy, known as Galaxy flavors. Preconfigured by the Galaxy community, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxy configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Network.” | concepts[62] |
| concept | Sentry — not defined in this paper's own words; automated client- and server-side error tracking and reporting — “Server monitoring and issue management is crucial in production Galaxy instances. Galaxy has integrated a plugin module to submit user bug-reports to configurable endpoints such as mailing lists or GitHub issues. With this, Galaxy can be configured to send error reports to a local ticket system. The recent integration of Sentry ( https://sentry.io/ ) for automated error tracking and reporting makes it easier for administrators to track both client- and server-side errors without requiring manual user bug reports.” | concepts[63] |
| concept | Galaxy Community Hub — not defined in this paper's own words; a collaboratively edited static site centralizing community resources — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | concepts[64] |
| concept | Markdown — not defined in this paper's own words; the simple source format used for community-editable Hub content — “Galaxy serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hub ( https://galaxyproject.org ) for all things Galaxy. The Hub uses a modified wiki approach, with content written in Markdown, a simple formatting language, and then built into a static website. Anyone can update the Markdown documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy community. The Hub includes a full list of public Galaxy servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxy and perform genomic analyses, extensive documentation on deploying and administering a Galaxy server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxy via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxy ).” | concepts[65] |
| concept | Biostars — not defined in this paper's own words; the question-and-answer forum supporting Galaxy users — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | concepts[66] |
| concept | Galaxy Training Network — not defined in this paper's own words; the community producing tutorials, workflows, slides, data, and tours — “The Main Galaxy server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobs by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostars ( 42 ) Question and Answers forum ( https://biostar.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxy user community provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Network has developed comprehensive tutorials and workflows for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxy tours ( https://doi.org/10.1101/225680 ).” | concepts[67] |
| concept | Intergalactic Utilities Commission — not defined in this paper's own words; the self-organizing community group defining tool-development best practices — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | concepts[68] |
| concept | Planemo — not defined in this paper's own words; a command-line tool for testing, validating, installing, and uploading Galaxy tools — “Another core area of community focus is tool development and availability. The Intergalactic Utilities Commission (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxy development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxy. To assist community members with tool development and distribution, a command-line tool named Planemo ( https://github.com/galaxyproject/planemo ) has been developed. Planemo provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.” | concepts[69] |
| concept | continuous integration — not defined in this paper's own words; automated execution of test suites on proposed code changes — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | concepts[70] |
| concept | codebase — not defined in this paper's own words; the Galaxy source whose proposed changes are automatically tested — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | concepts[71] |
| concept | Travis — not defined in this paper's own words; one continuous-integration service used by Galaxy — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | concepts[72] |
| concept | Jenkins — not defined in this paper's own words; one continuous-integration service used by Galaxy — “Community contributions have helped the Galaxy framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxy's scalability, functionality, and usability. The project utilizes the Travis and Jenkins continuous integration (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebase and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxy framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collections, workflows, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxy development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxy GitHub repository ( https://github.com/galaxyproject/galaxy/ ).” | concepts[73] |
Full text — tagged
The Galaxyterm platform for accessible, reproducible and collaborative biomedical analyses: 2018date update Abstract Galaxyterm (homepage: https://galaxyproject.org , main public server: https://usegalaxy.org ) is a web-based scientific analysis platformterm used by tens of thousands of scientists across the world to analyze large biomedical datasetsterm such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxyterm continues to focus on three key challenges of data-driven biomedical scienceterm: making analyses accessible to all researchers, ensuring analyses are completely reproducible , and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxyterm team and the open-source community around Galaxyterm have made substantial improvements to Galaxyterm's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxyterm to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShedterm. The Galaxy communityterm has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxyterm developer and user communities continue to grow and be integral to Galaxyterm's development. The number of Galaxyterm public servers, developers contributing to the Galaxyterm framework and its tools, and users of the main Galaxyterm server have all increased substantially.
INTRODUCTION Advances in biomedicine and biology increasingly rely on analysis of large datasets. Started in 2005, the Galaxy Projectterm ( https://galaxyproject.org ) ( 1–3 ) maintains a focus on enabling data-driven biomedical scienceterm by pursuing three goals: (a) accessible data analysisterm serving all scientists regardless of their informatics expertise and tool developers seeking a wider audience and broad integration of their tools; (b) reproducible analysesterm regardless of the particular platform and (c) transparent communicationterm of analyses, which in turn enables reuse and extension of analyses across communities of practice. The Galaxy Projectterm consists of four complementary componentsterm: The main public Galaxy serverterm ( https://usegalaxy.org )—this server is the subject of this article and has been online since 2007. It features a rich toolset for large-scale genomics analyses, terabytes of public data for use, and hundreds of shared analysis histories, workflowsterm, and interactive publication supplements. This server has more than 124,000 registered users whom run ∼245,000 analysis jobsterm each month.
The Galaxy framework and software ecosystemterm ( https://github.com/galaxyproject )—an open-source software package that anyone can use to run a Galaxyterm server on any Unix-based operating system. The Galaxyterm ecosystem includes a software development kitterm (SDK) for Galaxyterm tool development, API language bindingsterm for multiple programming languages, software for scripting Galaxyterm interactions, and tools for automating setup and deployment of Galaxyterm and its plugins such as tools and visualizations.
The Galaxy ToolShedterm ( https://toolshed.g2.bx.psu.edu/ )—a community-driven resource for the dissemination of Galaxyterm tools, workflowsterm, and visualizations. This server functions as an ‘AppStoreterm’ for Galaxyterm servers where developers and Galaxyterm admins can host, share, and install Galaxyterm tools, workflowsterm and visualizations.
The Galaxy Communityterm ( https://galaxyproject.org/community/ )—distinct and complementary subcommunities make key contributions to all aspects of the Project. These subcommunities address the needs and desires of every category of stakeholder including users, administrators, developers, resource providers and educators.
Galaxyterm has served hundreds of thousands of users, been used in >5700 scientific publications, and provided 500+ developers with a framework provisioning accessible, transparent, and reproducible data analysis ( https://galaxyproject.org/galaxy-project/statistics/ ). Many instances of the framework have been installed, including Galaxy Mainterm ( https://usegalaxy.org ) and over 99 publicly accessible servers ( https://galaxyproject.org/public-galaxy-servers/ ), serving biomedical and other domain-specific research. Significant growth has occurred across all sectors of the Galaxy Projectterm within the past two years (Figure 1 ).
Circular barplot illustrating recent growth of the Galaxy Projectterm across several independent facets. In the past two years, usage of the main public Galaxy serverterm has increased 60%, the number of tools and supported versions has increased 53%, and the amount of data analyzed on the main server has increased 72%. A growing number of public instances (18% increase) and cloud-based Galaxyterm instances (38% increase) provide researchers with a wider range of options for scalabilityterm and application domains. Additionally, more developers (45% increase with 63% more commits to the codebaseterm) contributed to the Galaxy framework and software ecosystemterm. Question and answer activity on the Galaxyterm Biostarsterm forum increased 68%.
NEW FEATURES Scalabilityterm Scalabilityterm is amongst the most significant challenges that Galaxyterm faces as the size and number of biomedical and especially genomics datasets continues to grow. For instance, single-cell RNA-seq experiments routinely generate hundreds or thousands of primary datasets. As a web-based application, Galaxyterm must scale both in its web-based interface and on its backend server and do so in a multiuser environment.
User interface scalabilityterm enables scientists to use the Galaxyterm web interface to analyze many datasets, apply (collective) operations on them, and design pipelines to analyze them. Galaxyterm implements a variety of features to facilitate analyzing large numbers of datasets, including workflowsterm and collectionsterm . Our recent optimizations of the user interface (UI) yielded a significant improvement to frontend scalabilityterm. We benchmarked the optimizations by replicating an experiment conducted on single Hematopoietic stem cells and multipotent progenitors ( 4 ) to quantify the expression of 64 000 transcripts, which generates 11 872 history items. Galaxyterm ran this proof of concept experiment seamlessly using existing standard tools, whereas earlier versions of Galaxyterm would not have been able to support this analysis.
Server scalabilityterm refers to the Galaxyterm's ability to execute many data analysis/manipulation tasks for many users. This is achieved by advantageously utilizing a range of available computing resources. The Galaxyterm framework runs on various platforms, from a standard laptop to institutional clusters and cloud-based platforms. Galaxyterm is highly versatile in its ability to deploy jobsterm (atomic units of work), as it can leverage a multitude of workload managersterm including Slurm ( 5 ), HTCondor ( 6 ), Apache Mesos ( 7 ) and Kubernetes ( https://kubernetes.io ), among others, in addition to a built-in lightweight jobterm running system. Recent enhancements to Galaxyterm's jobterm management include dynamic job destination assignmentterm (which facilitate automatic jobterm parameter-specific resource selection), delay in jobterm queuing (e.g. for workflowsterm), automatic jobterm re-submission (e.g., on jobterm failure due to a temporary cluster error), and means of implementing fair-share prioritization schemes. These features are being used on Galaxy Mainterm (Figure 2 ) to leverage cloud computing resources for better jobterm throughput. Specifically, Galaxy Mainterm is now configured to take advantage of the XSEDE infrastructureterm ( 8 ) that includes Bridges and Stampede resources as well as the Jetstream cloudterm ( 9 ). The benefits of using these resources include the ability to run larger jobsterm, as shown in Figure 3 . Additionally, use of these resources has enabled new types of analysis to be enabled on Main. Notably, this includes Galaxy Interactive Environmentsterm through the ability to use containerization technologiesterm and provide sufficient isolation of individual jobsterm from other processes running on the same underlying compute infrastructure.
Schematic of servers and services in use at Galaxy Mainterm. ( A ) A global overview of Galaxy Mainterm resources. When users interact with usegalaxy.org, their browser connects to one of two frontends (shown as web-01/02) with file uploads being handled by web-03/04; each of these web servers connects to a database server and mounts a set of shared distributed file systems. Web-03/04 also prepares and schedules jobsterm using Slurm directly to manage compute tasks on fifteen dedicated compute nodes, which also directly mount the shared distributed file systems. A combination of Slurm and Pulsar ( https://github.com/galaxyproject/pulsar ) are used to manage tasks and for dataset file staging, respectively, on the Jetstream cloudterm at Indiana University (IU) and the Texas Advanced Computing Center (TACC). Communication between Galaxyterm and Pulsar is handled using the RabbitMQ ( https://www.rabbitmq.com/ ) message broker. Additional jobsterm are sent to the supercomputer systems Bridges at Pittsburgh Supercomputing Center (PSC) and Stampede at TACC using Pulsar. These various compute resources are chosen based upon tool and jobterm characteristics. See, e.g. https://github.com/galaxyproject/usegalaxy-playbook/wiki/Infrastructure for specific and up-to-date information. ( B ) Multiple frontend servers provide Galaxyterm content to users by utilizing round-robin load balancing. Nginx ( https://nginx.org/ ) is used to serve HTTP content from the Galaxyterm uWSGIterm web application. Individual software processes are monitored and controlled using Supervisor ( http://supervisord.org/ ). Each of these frontend servers connects to a PostgreSQL ( https://www.postgresql.org/ ) database server. ( C ) Layout of data schemes used by Galaxy Mainterm is optimized for application speed, concurrent access, and versioned content. Each Galaxyterm frontend server utilizes a combination of shared distributed file systems, CVMFS for versioned semi-static content and TACC’s Corral filesystem via NFS for mutable content, along with server-specific local file systems. (D) CernVM File Systemterm (CVMFS) infrastructure hosted by the Galaxy Projectterm that is used at Main and available for access to any other Galaxyterm instance. Stratum 0 contains the single-source modifiable data repositories. File content is served using the Apache HTTP server ( https://httpd.apache.org/ ). To enable redundancy and scaling to a large number of clients, Stratum 1 replica servers are hosted at multiple locations and utilize Squid ( http://www.squid-cache.org/ ) for data caching. Additional replica servers can also be hosted by community members. Individual clients (Galaxyterm instances and compute nodes) access data content from Stratum 1 servers using a Filesystem in Userspace (FUSE) mount.
Enabling automated selection and use of specialized national cyberinfrastructure compute resources from Galaxy Mainterm enhances user-experience. It is now possible to run jobsterm that are up to an order of magnitude larger than before by using Bridges and Stampede. New types of jobsterm, such as interactive environments (see Advances in tools section), that require execution isolation due to security concerns are enabled by utilizing virtualization facilitated by the Jetstream cloudterm. Consequently, it is possible to concurrently run more jobsterm due to the increase in processing capacity.
A complete Galaxyterm server with a full repertoire of tools and reference data can be run on major cloud platforms. These servers are launched independently by users, and come pre-configured with hundreds of tools and reasonable default settings typical of a production server. Notably, launched instances do not have usage quotas and can be customized to install any desired tool. We have designed a cloud-agnostic approach for leveraging these resources by developing the abstraction library CloudBridgeterm ( 10 ) and a new CloudLaunchterm application. These two solutions make it possible to launch Galaxyterm instances across a variety of cloud providers while reducing the requirement to build and maintain cloud-specific resources (e.g. machine images, file systems). There are now 10 different flavors of Galaxyterm available for launching on major clouds including Amazon Web Services, Jetstream and Microsoft Azure ( https://launch.usegalaxy.org ).
Advances in tools The Galaxy ToolShedterm ( 11 ) assumes the role of an AppStoreterm for Galaxyterm instances by hosting thousands of tools. The ToolShed improves tool availability, deployment, and portability across Galaxyterm servers and computing environments.
Updated tool suite Over the last two years, we have expanded both the quantity and quality of the tools available on the Galaxy ToolShedterm. As of April 2018date, the ToolShed hosts 5628 tools, which shows 53% growth since 2016, and ∼2000 repositories had at least one new update. Examples of new tools include: GEMINI for exploring genetic variation ( 12 ); mothur for analyzing rRNA gene sequences ( 13 ); QIIME for quantitative microbiome analysis from raw DNA sequencing data ( 14 ); deepTools for explorative analysis of deeply sequence data ( 15 , 16 ); HiCexplorer ( 17 ) for analysis and visualization of Hi-C data; ChemicalToolBox for comprehensive access to cheminformatics libraries and drug discovery tools ( 18 ); minimap2 ( https://arxiv.org/abs/1708.01492 ) and poretools for long read sequencing analysis ( 19 ); MultiQC ( 20 ) to aggregate multiple results into a single report; a new RNA-seq analysis tool suite with modern analysis tools such as Kallisto ( 21 ), Salmon ( 22 ), Deseq2 ( 23 ) and STAR-Fusion ( 24 ), and GenomeSpace ( 25 ), a cloud-based interoperability tool.
Tool environment and interface The portability and backward-compatibility of the Galaxyterm tools environment is improved significantly. Accordingly, a tool configuration now includes a tool profile versionterm , which is used to ensure compatibility between a version of a tool and its targeted Galaxyterm version. In addition, tool profile versionsterm allow for the evolution of new and better tool defaults and behaviors while maintaining backwards compatibility. We also improved the ToolShed API and its interface to facilitate installing tools missing from an imported workflowterm. We improved the installation process so that restarting Galaxyterm is not required to use a newly installed tool.
Interactive analysis and visualization Galaxyterm's UI makes it possible for anyone to run complex analyses. However, a complete analysis of genomic data often requires custom scripts or visualizations, especially at the beginning (data preparation) or end (data summarization) of analyses. To meet these customized needs, we recently introduced Galaxy Interactive Environmentsterm ( 26 ), an integration of Galaxyterm with Jupyterterm (RStudioterm is in development)—a commonly used interactive scripting platform. With Interactive Environments, Galaxyterm users benefit from existing computational infrastructure via both graphical UI and ad hoc scripting, or any combination of these.
Galaxyterm's visualization frameworkterm ( 27 ) makes it possible to integrate a wide variety of Web-based and server-side visualizations. Through this framework, many new visualizations have been added to Galaxyterm, including Cytoscape ( 28 ), and the WebGL enabled 3D Protein viewer NGL ( 29 ), molecular interaction networks and macromolecular structures visualizations, and the 100+ visualizations available through BioJS ( 30 ), a rich set of community-driven JavaScript components for agile and interactive visualization of biological data.
User interface and experience enhancements There are two common modes of data analysis: exploratoryterm and pipeline executionterm. Galaxyterm enables simultaneous access to both of these. Users are able to interactively analyze their data by making use of individual tools in a trial-and-error manner. They are then able to automatically generate reusable and generalizable workflowsterm from an ad hoc analysis. An interactive workflowterm editor is also available to modify or generate workflowsterm from scratch. At any point in time, a user can seamlessly switch modes between interactively analyzing datasets and executing a workflowterm on these datasets. There is no analysis lock-in, and users can exercise full control, or make use of pre-existing pipelines. Importantly, these analysis artefacts, such as datasets, analysis histories, workflowsterm, and visualizations can all be shared and copied by collaborators at the discretion of the analyst.
Client-side infrastructure The client-side of Galaxyterm, which is the user-interface most people associate with Galaxyterm, has seen significant changes under the hood. The usage of server-side mako templates, for example to create forms, has been further reduced and replaced by client-side only code that communicates via the RESTful Galaxy APIterm with the backend. This minimizes the number of full-page refreshes and improves response timeterm by enabling partial page updates. The interface has been further enhanced to allow for drag-and-drop of files and datasets, presents a fuzzy search on dataset and tool metadata, and implements a modal scratchbook for visualizations and comparison of multiple datasets.
Furthermore, the community has selected the Vue.jsterm framework ( https://vuejs.org/ ) as the base for future improvements allowing all UI elements to converge into a more reactive and future-proof interface. With the integration of Vue.jsterm, the entire client-side build system was updated to utilize the latest web-technologies, to make routing and loading times faster, and to encourage rapid future interface improvements. While mostly transparent to users, these changes are the fundamental groundwork of a much more flexible UI framework that will enable visual enhancements and an improved user experience for years to come.
Tagsterm Although tagsterm have been supported in Galaxyterm for several years, they have only recently become advantageous for large many-sample analyses. We have enhanced tagsterm to allow propagation through dataset analysis steps. This facilitates tracking individual datasets through the entire analysis life-cycle and becomes part of the provenance systemterm and ease-of-use of Galaxyterm. To enable automatic tagterm propagation, a hash-sign (#) is placed at the beginning of the tagterm, which is colloquially referred to as a named-tagterm. While standard Galaxyterm output dataset naming is suitable for many interactive analyses, the connection between inputs and outputs through large workflowsterm becomes increasingly less obvious; by utilizing named-tagsterm, users can label datasets with an identifier that is maintained throughout the analysis.
Webhooksterm Inspired by user feedback and the need to quickly modify and adapt Galaxyterm's interface, we integrated a pluggable system to extend Galaxyterm's frontend. Webhooksterm provide an entry-point into the Galaxyterm UI, in which it is possible to add buttons, menu entries, or entire iframes. At these entry-points a developer can dynamically add client-side codeterm (JavaScript, HTML, CSS) and interact with the rest of the Galaxyterm user-interface. By integrating Webhooksterm with the Galaxy APIterm, it is also possible to trigger server-side functions from within a Webhookterm. Webhooksterm can be thoroughly customized and are enabled at the discretion of the Galaxyterm administrator.
Interactive toursterm We have developed self-paced, interactive toursterm that users can step through to learn about Galaxyterm. These tours guide users step by step through using the interface including tools, workflowsterm, and other features available in Galaxyterm. To simplify tour creation, a Tour Builderterm ( https://github.com/TailorDev/galaxy-tourbuilder ) has been created for recording, replaying, updating and exporting tours.
Improved workflowsterm Galaxyterm workflowsterm have been extended in several ways. Switching between tool versions and upgrading workflowsterm with new tool versions is now supported. A workflowterm can now be embedded in another, making it easier to create and edit workflowsterm that have many common steps repeated. Many of these features have existed in in standalone workflowterm systems, such as Taverna ( 31 ), for sometime, but have been widely requested by Galaxyterm users. Workflowsterm are now scheduled by a Galaxyterm server more efficiently and in the background, making it possible to execute larger workflowsterm, generating tens of thousands of jobsterm, while providing instant feedback and a snappier user-experience. We have also enhanced Galaxyterm with initial support for running workflowsterm defined in the Common Workflow Languageterm ( 32 ) format.
Dataset collectionsterm Galaxy Dataset Collectionsterm combine datasets to enable simultaneous analysis. They organize sets of datasets as potentially nested lists of objects allowing easier data handling and batch execution of tools. In addition to the related frontend improvements, and support of nesting collectionsterm together, we recently introduced specialized tools to be executed on collectionsterm (e.g. Collapse , which combines a list of datasets into a single dataset, Flatten which takes nested collectionsterm and produces a flat list of datasets, and Merge which takes two lists and creates a single unified list), and enabled uploading and downloading dataset collectionsterm to and from both user's local disk and Galaxyterm data libraries.
Infrastructure enhancements In order to make Galaxyterm more robust in a production environment, we adopted technologies to enhance Galaxyterm's portability, security, reliability, and scalabilityterm. Galaxyterm now utilizes uWSGIterm ( http://projects.unbit.it/uwsgiterm ) as its default web application server. This adoption has several advantages, namely the ability to negate Python's limitations regarding concurrent tasks execution, built-in load balancing, scalabilityterm, improved fault tolerance and the possibility of restarting Galaxyterm uninterruptedly.
Many tools available via Galaxyterm rely on the availability of reference and index data. To promote ease of use and efficient storage and compute resources, Galaxyterm is able to share a precomputed set of local reference data for tools to use. Previously, making this data available to the tools was a time intensive process where a Galaxyterm administrator had to install and properly configure the server, either manually or by using Data Managers ( 33 ). However, this resulted in much redundant effort required for each Galaxyterm server being configured. To streamline this process, we have made all the reference data we prepared for Galaxy Mainterm available via a CernVM File Systemterm (CVMFS; ( 34 )), a scalable and content-addressable file system. This repository currently hosts 5TB of pre-build reference data, which are versioned and shared publicly with read-only access. With minimal configuration, any instance of Galaxyterm, including Galaxy-Dockerterm images, can attach to this file system and gain access to the same reference data available on Galaxy Mainterm. To improve accessibility and fault-tolerance, this data source is replicated on servers located in Europe and Australia.
Galaxyterm is powered by various open-source projects which are installed automatically, and used when needed. Galaxyterm is using the Conda package managerterm ( https://conda.io ) as its default tool dependency resolver, and offers support for virtualization and containerization technologiesterm (e.g. Dockerterm ( https://www.dockerterm.com ) and Singularityterm ( 35 )) to ensure a higher level of portability, if needed. By leveraging the Biocondaterm ( https://doi.org/10.1101/207092 ) and the BioContainerterm ( 36 ) projects, Galaxyterm is able to provision and use reproducible tool execution environmentsterm (( 37 ); https://doi.org/10.1101/200683 ).
Galaxyterm is a generic data analysis framework, which can be configured for various application scenarios using a wide range of configuration parameters. To facilitate configuring these parameters with optimal values for a number of predefined application scenarios, the Galaxy projectterm leverages Ansibleterm ( https://www.ansibleterm.com ), software for automated configuration and management of other software packages. We have developed and shared Ansibleterm configurations for Galaxy Mainterm , the main public Galaxy serverterm, ( https://github.com/galaxyproject/usegalaxy-playbook ) and also a configurable generic playbook for setting up production instances on cloud resources, virtual machines, and bare metal ( https://github.com/ARTbio/GalaxyKickStart ). This playbook can be used as a reference for configuring a Galaxyterm instance for a production environment.
The Galaxy-Dockerterm project ( https://github.com/bgruening/docker-galaxy-stable ), delivers a production ready Galaxyterm instance in minutes and can be used as the basis for personalized, self-contained, portable instances of Galaxyterm, known as Galaxy flavorsterm. Preconfigured by the Galaxy communityterm, a plenitude of flavors already exist covering application scenarios, from BLAST+ ( 38 , 39 ), metagenomics ( https://doi.org/10.1101/183970 ), ChIP-exo analysis, or RNA research ( 40 ). In addition to the facilitated and out-of-box functionality, these images provision isolated environments well-suited for experimenting with tools and Galaxyterm configurations, and are ideal for training courses, as demonstrated by the Galaxy Training Networkterm.
Server monitoring and issue management is crucial in production Galaxyterm instances. Galaxyterm has integrated a plugin module to submit user bug-reports to configurable endpoints such as mailing lists or GitHub issues. With this, Galaxyterm can be configured to send error reports to a local ticket system. The recent integration of Sentryterm ( https://sentryterm.io/ ) for automated error tracking and reporting makes it easier for administrators to track both client- and server-side errors without requiring manual user bug reports.
COMMUNITY Galaxyterm serves several distinct communities: researchers, tool developers, resource providers, trainers, and trainees. To centralize resources for all communities, we have developed the Galaxy Community Hubterm ( https://galaxyproject.org ) for all things Galaxyterm. The Hub uses a modified wiki approach, with content written in Markdownterm, a simple formatting language, and then built into a static website. Anyone can update the Markdownterm documents using GitHub pull requests, a standard approach for collaborating on code and documentation on GitHub projects. Submitted pull requests are reviewed and merged, and the Hub site is automatically regenerated and updated, resulting in high-quality reviewed content that can be updated by any member of the Galaxy communityterm. The Hub includes a full list of public Galaxyterm servers ( https://galaxyproject.org/public-galaxy-servers ), a large set of tutorials for learning to use Galaxyterm and perform genomic analyses, extensive documentation on deploying and administering a Galaxyterm server in the Cloud or on local hardware, and upcoming events. We also maintain an annotated listing of the >5000 publications referencing Galaxyterm via the free and open-source Zotero service ( https://www.zotero.org/groups/1732893/galaxyterm ).
The Main Galaxyterm server has over 124 000 registered users and ∼2000 new users register each month. On average, 20 000 unique users execute over 245 000 analysis jobsterm by accessing 750 different tools every month. With such an active user-base, questions on platform and tool usage, as well as general research questions ( 41 ), are common. To efficiently assist users in performing research, we provide a Biostarsterm ( 42 ) Question and Answers forum ( https://biostarterm.usegalaxy.org/ ) that leverages the knowledge and strength of community members to provide support. This forum is monitored and moderated by core team members, but the Galaxyterm user communityterm provides many answers. Help is also available through live chat with the team and community members via Gitter and IRC chat services, which are used most often by developers and administrators. In addition to the online help and documentation, the Galaxy Training Networkterm has developed comprehensive tutorials and workflowsterm for performing common data analysis tasks, providing topic-specific introduction slides, hands-on material, sample data, and even playable Galaxyterm tours ( https://doi.org/10.1101/225680 ).
Many in-person events that highlight and build the Galaxy communityterm occur each year ( https://galaxyproject.org/events/ ). These include free or low-cost hands-on workshops and training sessions that have been hosted by the community on six continents. The Galaxy Communityterm Conference (GCC) is an annual conference that was first held in 2010. GCC alternates between Europe and the United States, includes two full days of training, two days of coding and data analysis hackathons, and two days of oral and poster presentations. Galaxyterm conferences have had over two hundred attendees each year since 2012, and over eleven hundred different researchers have attended since 2010. Our 2018date conference will be hosted jointly with the Bioinformatics Open Source Conference (BOSC) in an effort to promote and centralize discussion of open-source software for bioinformatics.
Another core area of community focus is tool development and availability. The Intergalactic Utilities Commissionterm (IUC; https://galaxyproject.org/iuc/ ) is a community-based organization that defines best-practices for tool development that help ensure the availability of high-quality tools in the ToolShed. It is a self-organizing and self-regulating group that has grown by six new members in the last two years and is primarily composed of individuals outside of the core Galaxyterm development team. The IUC is only one of many tool contributors, with the ToolShed allowing any member of the community to share tools that they have added to Galaxyterm. To assist community members with tool development and distribution, a command-line tool named Planemoterm ( https://github.com/galaxyproject/planemoterm ) has been developed. Planemoterm provides functionality for verifying best-practice adherence, testing, installation and uploading of tools to the ToolShed.
Community contributions have helped the Galaxyterm framework and its tool suite to grow considerably. One hundred and seventy-four developers, who have collectively produced 13 135 commits within just the past two years (63% increase since January 2016), have improved Galaxyterm's scalabilityterm, functionality, and usability. The project utilizes the Travisterm and Jenkinsterm continuous integrationterm (CI) services to automatically execute comprehensive test suites on each set of proposed code changes. This strategy helps prevent the introduction of bugs to the codebaseterm and improves review time. By harnessing the open-source community and modern software development practices, we are able to release a new stable version of the Galaxyterm framework every four months. Current future directions include enabling data and compute federation; tighter coupling of Interactive Environments with provenance and reuse; ToolShed installation and development enhancements; continued work on collectionsterm, workflowsterm, analysis interfaces and history views; additional training material; improving statistical usage tracking and instrumentation; and much more. For anyone interested in getting involved with Galaxyterm development, we invite them to read the project's Contributing and Code of Conduct documents, review open issues, and explore the current roadmap, all which are available from the Galaxyterm GitHub repository ( https://github.com/galaxyproject/galaxyterm/ ).
ACKNOWLEDGEMENTS The Galaxy Projectterm has grown in large part thanks to the contributions of time and effort by numerous individuals over the years. Contributing individuals include members of the Galaxyterm user, developer and administrative communities and organizers of Galaxy Communityterm Conferences. We are indebted to these helpful people. The Public Galaxyterm site is located at the Texas Advanced Computing Center (TACC at the University of Texas). We are extremely grateful to both TACC and CyVerse for enabling Galaxyterm to serve thousands of researchers worldwide.
FUNDING National Human Genome Research Institute, National Institutes of Health [HG006620, HG005133, HG004909 and HG005542]; NSF [DBI 0543285, 0850103 and 1661497]; Huck Institutes for the Life Sciences at Penn State; and, in part, under a grant with the Pennsylvania Department of Health using Tobacco Settlement Funds, the Department specifically disclaims responsibility for any analyses, interpretations or conclusions. Funding for open access charge: Cleveland Clinic.
Conflict of interest statement . None declared.
REFERENCES