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

YesGalaxy (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.NIH [U41 HG006620, U24 HG010263, U24 CA231877, U01 CA253481]; US National Science Foundation [1661497, 1758800, 2216612]; computational resources are provided by the Advanced Cyberinfrastructure Coordination Ecosystem (ACCESS-CI), Texas Advanced Computing Center, and the JetStream2 scientific cloud. Funding for open access charge: NIH. ELIXIR IS and Travel grants; EU Horizon Europe [HORIZON-INFRA-2021-EOSC-01-04, 101057388]; EU Horizon Europe under the Biodiversity, Circular Economy and Environment program (REA.B.3, BGE 101059492); German Federal Ministry of Education and Research, BMBF [031 A538A de.NBI-RBC]; Ministry of Science, Research and the Arts Baden-Württemberg (MWK) within the framework of LIBIS/de.NBI Freiburg. Galaxy Australia is supported by the Australian BioCommons which is funded through Australian Government NCRIS investments from Bioplatforms Australia and the Australian Research Data Commons, as well as investment from the Queensland Government RICF program

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

YesGalaxy (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.NIH [U41 HG006620, U24 HG010263, U24 CA231877, U01 CA253481]; US National Science Foundation [1661497, 1758800, 2216612]; computational resources are provided by the Advanced Cyberinfrastructure Coordination Ecosystem (ACCESS-CI), Texas Advanced Computing Center, and the JetStream2 scientific cloud. Funding for open access charge: NIH. ELIXIR IS and Travel grants; EU Horizon Europe [HORIZON-INFRA-2021-EOSC-01-04, 101057388]; EU Horizon Europe under the Biodiversity, Circular Economy and Environment program (REA.B.3, BGE 101059492); German Federal Ministry of Education and Research, BMBF [031 A538A de.NBI-RBC]; Ministry of Science, Research and the Arts Baden-Württemberg (MWK) within the framework of LIBIS/de.NBI Freiburg. Galaxy Australia is supported by the Australian BioCommons which is funded through Australian Government NCRIS investments from Bioplatforms Australia and the Australian Research Data Commons, as well as investment from the Queensland Government RICF program.Please note, contributors are listed in alphabetical order

1H magnetic resonance spectroscopy of nanomelic chicken cartilage: effect of aggrecan depletion on cartilage T2

AbstractObjective: To determine the effect of proteoglycan depletion on cartilage proton magnetic resonance (MR) spectroscopy T2using nanomelic chicken cartilage, a genetic mutant that completely lacks aggrecan.Design: Proton MR spectroscopic T2measurements of normal embryonic and nanomelic femoral epiphyseal cartilage were obtained using a 96-echo pulse sequence with inter-echo delay times increased logarithmically over the TE period of 60 μs to 1.7 s. The relative intensity and distribution of cartilage T2components were determined by fitting signal decay curves to a multi-exponential function. The number of T2components in the signal decay curves was determined by the degree of freedom limited r2of the fit.Results: For normal fetal chicken cartilage, 97.6±0.2% (mean±95% confidence interval) of the total signal comprises a long T2component (179.1±1.3 ms) with a relatively small short T2component (0.5±0.4 ms). The T2distribution for nanomelic cartilage is more heterogeneous with four components identified: two short T2components (0.5±0.02 and 7.3±0.6 ms), a large intermediate component (56.4±5.6 ms), and a broadly distributed long component (137.5±16.6 ms). In nanomelic cartilage there is greater heterogeneity of cartilage T2indicating greater variation in water proton mobility and exchange of water with the extracellular matrix.Conclusion: Absence of aggrecan in the extracellular cartilage matrix produces greater heterogeneity in cartilage T2, but will not increase T2as has been previously reported with degenerative change of the collagen matrix

Osteoarthritis year 2013 in review: imaging

SummaryPurposeTo review recent original research publications related to imaging of osteoarthritis (OA) and identify emerging trends and significant advances.MethodsRelevant articles were identified through a search of the PubMed database using the query terms “OA” in combination with “imaging”, “radiography”, “MRI”, “ultrasound”, “computed tomography”, and “nuclear medicine”; either published or in press between March 2012 and March 2013. Abstracts were reviewed to exclude review articles, case reports, and studies not focused on imaging using routine clinical imaging measures.ResultsInitial query yielded 932 references, which were reduced to 328 citations following the initial review. MRI (118 references) and radiography (129 refs) remain the primary imaging modalities in OA studies, with fewer reports using computed tomography (CT) (35 refs) and ultrasound (23 refs). MRI parametric mapping techniques remain an active research area (33 refs) with growth in T2*- and T1-rho mapping publications compared to prior years. Although the knee is the major joint studied (210 refs) there is interest in the hip (106 refs) and hand (29 refs). Imaging continues to focus on evaluation of cartilage (173 refs) and bone (119 refs).ConclusionImaging plays a major role in OA research with publications continuing along traditional lines of investigation. Translational and clinical research application of compositional MRI techniques is becoming more common driven in part by the availability of T2 mapping data from the Osteoarthritis Initiative (OAI). New imaging techniques continue to be developed with a goal of identifying methods with greater specificity and responsiveness to changes in the joint, and novel functional neuroimaging techniques to study central pain. Publications related to imaging of OA continue to be heavily focused on quantitative and semiquantitative MRI evaluation of the knee with increasing application of compositional MRI techniques in the hip