The Galaxy platform for accessible, reproducible, and collaborative data analyses:2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.</p

The Galaxy platform for accessible, reproducible, and collaborative data analyses:2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO2 emissions generated by each Galaxy job.</p

Game of Templates. Deploying and (re-)using Virtualized Research Environments in High-Performance and High-Throughput Computing

The Virtual Open Science Collaboration Environment project worked on different use cases to evaluate the necessary steps for virtualization or containerization especially when considering the external dependencies of digital workflows. Virtualized Research Environments (VRE) can both help to broaden the user base of an HPC cluster like NEMO and offer new forms of packaging scientific workflows as well as managing software stacks. The eResearch initiative on VREs sponsored by the state of Baden-Württemberg provided the necessary framework for both the researchers of various disciplines as well as the providers of (large-scale) compute infrastructures to define future operational models of HPC clusters and scientific clouds. In daily operations, VREs running on virtualization or containerization technologies such as OpenStack or Singularity help to disentangle the responsibilities regarding the software stacks needed to fulfill a certain task. Nevertheless, the reproduction of VREs as well as the provisioning of research data to be computed and stored afterward creates a couple of challenges which need to be solved beyond the traditional scientific computing models

Current and future options for the management of phantom-limb pain

Phantom-limb pain (PLP) belongs among difficult-to-treat chronic pain syndromes. Treatment options for PLP are to a large degree implicated by the level of understanding the mechanisms and nature of PLP. Research and clinical findings acknowledge the neuropathic nature of PLP and also suggest that both peripheral as well as central mechanisms, including neuroplastic changes in central nervous system, can contribute to PLP. Neuroimaging studies in PLP have indicated a relation between PLP and the neuroplastic changes. Further, it has been shown that the pathological neuroplastic changes could be reverted, and there is a parallel between an improvement (reversal) of the neuroplastic changes in PLP and pain relief. These findings facilitated explorations of novel neuromodulatory treatment strategies, adding to the variety of treatment approaches in PLP. Overall, available treatment options in PLP include pharmacological treatment, supportive non-pharmacological non-invasive strategies (eg, neuromodulation using transcranial magnetic stimulation, visual feedback therapy, or motor imagery; peripheral transcutaneous electrical nerve stimulation, physical therapy, reflexology, or various psychotherapeutic approaches), and invasive treatment strategies (eg, surgical destructive procedures, nerve blocks, or invasive neuromodulation using deep brain stimulation, motor cortex stimulation, or spinal cord stimulation). Venues of further development in PLP management include a technological and methodological improvement of existing treatment methods, an implementation of new techniques and products, and a development of new treatment approaches

FAIR data retrieval for sensitive clinical research data in Galaxy

Background: In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized “omics” platform for FAIR data analysis. Results: To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. Conclusions: We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.</p

A Sorting Hat For Clusters. Dynamic Provisioning of Compute Nodes for Colocated Large Scale Computational Research Infrastructures

Current large scale computational research infrastructures are composed of multitudes of compute nodes fitted with similar or identical hardware. For practical purposes, the deployment of the software operating environment to each compute node is done in an automated fashion. If a data centre hosts more than one of these systems – for example cloud and HPC clusters – it is beneficial to use the same provisioning method for all of them. The uniform provisioning approach unifies administration of the various systems and allows flexible dedication and reconfiguration of computational resources. In particular, we will highlight the requirements on the underlying network infrastructure for unified remote boot but segregated service operations. Building upon this, we will present the Boot Selection Service, allowing for the addition, removal or rededication of a node to a given research infrastructure with a simple reconfiguration

Galaxy training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform

Desenvolvimento de habilidades cognitivas para a leitura e escrita

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de.NBI Cloud federation through ELIXIR AAI

Belmann P, Fischer B, Krüger J, et al. de.NBI Cloud federation through ELIXIR AAI. F1000Research. 2019;8: 842.The academic de.NBI Cloud offers compute resources for life science research in Germany.  At the beginning of 2017, de.NBI Cloud started to implement a federated cloud consisting of five compute centers, with the aim of acting as one resource to their users. A federated cloud introduces multiple challenges, such as a central access and project management point, a unified account across all cloud sites and an interchangeable project setup across the federation. In order to implement the federation concept, de.NBI Cloud integrated with the ELIXIR authentication and authorization infrastructure system (ELIXIR AAI) and in particular Perun, the identity and access management system of ELIXIR. The integration solves the mentioned challenges and represents a backbone, connecting five compute centers which are based on OpenStack and a web portal for accessing the federation.This article explains the steps taken and software components implemented for setting up a federated cloud based on the collaboration between de.NBI Cloud and ELIXIR AAI. Furthermore, the setup and components that are described are generic and can therefore be used for other upcoming or existing federated OpenStack clouds in Europe

Training Infrastructure as a Service

Background Hands-on training, whether in bioinformatics or other domains, often requires significant technical resources and knowledge to set up and run. Instructors must have access to powerful compute infrastructure that can support resource-intensive jobs running efficiently. Often this is achieved using a private server where there is no contention for the queue. However, this places a significant prerequisite knowledge or labor barrier for instructors, who must spend time coordinating deployment and management of compute resources. Furthermore, with the increase of virtual and hybrid teaching, where learners are located in separate physical locations, it is difficult to track student progress as efficiently as during in-person courses. Findings Originally developed by Galaxy Europe and the Gallantries project, together with the Galaxy community, we have created Training Infrastructure-as-a-Service (TIaaS), aimed at providing user-friendly training infrastructure to the global training community. TIaaS provides dedicated training resources for Galaxy-based courses and events. Event organizers register their course, after which trainees are transparently placed in a private queue on the compute infrastructure, which ensures jobs complete quickly, even when the main queue is experiencing high wait times. A built-in dashboard allows instructors to monitor student progress. Conclusions TIaaS provides a significant improvement for instructors and learners, as well as infrastructure administrators. The instructor dashboard makes remote events not only possible but also easy. Students experience continuity of learning, as all training happens on Galaxy, which they can continue to use after the event. In the past 60 months, 504 training events with over 24,000 learners have used this infrastructure for Galaxy training