Integration of EGA secure data access into Galaxy

Hoogstrate, Youri; Zhang, Chao; Senf, Alexander; Bijlard, Jochem; Hiltemann, Saskia; Enckevort, David van; Repo, Susanna; Heringa, Jaap; Jenster, Guido; Fijneman, Remond J.A.; Boiten, Jan-Willem; Meijer, Gerrit A.; Stubbs, Andrew; Rambla, Jordi; Spalding, J. Dylan; Abeln, Sanne

doi:10.12688/f1000research.10221.1

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…Once the users in tranSMART retrieve EGA file IDs, they can directly import the raw data files into a Galaxy instance with the Galaxy tool “EGA download streamer” 26 . Subsequently, the workflow in Galaxy can be applied to these data for reproduction or new analysis.…”

Section: Resultsmentioning

confidence: 99%

“…A Galaxy tool called ega_download_streamer 26 was used, which wraps EGA download client into Galaxy. We set up an EGA account with access to TraIT-CLUC data into Galaxy.…”

Section: Methodsmentioning

confidence: 99%

“…Here we report the full outcome of this implementation study; previously, we described the connection between Galaxy and EGA 26 . In this paper, we show a proof of concept that demonstrates the feasibility of linking data resources for reusing archived data, with the help of the TraIT Cell Line Use Case (TraIT-CLUC) data.…”

Section: Introductionmentioning

confidence: 99%

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Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

et al. 2017

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The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

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Section: Resultsmentioning

confidence: 99%

“…A Galaxy tool called ega_download_streamer 26 was used, which wraps EGA download client into Galaxy. We set up an EGA account with access to TraIT-CLUC data into Galaxy.…”

Section: Methodsmentioning

confidence: 99%

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Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

et al. 2017

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“…The enormous amount of data generated on high‐throughput molecular tests is addressed in Reference . They propose a framework to download and use data from the European Genome‐phenome Archive.…”

Section: Related Workmentioning

confidence: 99%

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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“…This is particularly important as we reach the cusp of widespread implementation of genomic and personalised medicine.Our study also highlighted some limitations. For instance, adequate public databases for multi-dimensional open access data, equivalent to dbGaP[62] or EGA[63] for controlled access data, are currently still lacking. Consequently, the PGP-UK data were submitted to multiple open access public databases, depending on type of data.…”

mentioning

confidence: 99%

PGP-UK: a research and citizen science hybrid project in support of personalized medicine

Beck

Bignell

et al. 2018

Preprint

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Molecular analyses such as whole-genome sequencing have become routine and are expected to be transformational for future healthcare and lifestyle decisions. Population-wide implementation of such analyses is, however, not without challenges, and multiple studies are ongoing to identify what these are and explore how they can be addressed. Defined as a research project, the Personal Genome Project UK (PGP-UK) is part of the global PGP network and focuses on open data sharing and citizen science to advance and accelerate personalized genomics and medicine. Here we report our findings on using an open consent recruitment protocol, active participant involvement, open access release of personal genome, methylome and transcriptome data and associated analyses, including 47 newvariants predicted to affect gene function and innovative reports based on the analysis of genetic and epigenetic variants. For this pilot study, we recruited ten participants willing to actively engage as citizen scientists with the project. In addition, we introduce Genome Donation as a novel mechanism for openly sharing previously restricted data and discuss the first three donations received. Lastly, we present GenoME, a free, open-source educational app suitable for the lay public to allow exploration of personal genomes. Our findings demonstrate that citizen science-based approaches like PGP-UK have an important role to play in the public awareness, acceptance and implementation of genomics and personalized medicine.

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Integration of EGA secure data access into Galaxy

Cited by 8 publications

References 15 publications

Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

PGP-UK: a research and citizen science hybrid project in support of personalized medicine

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