Resources & Training
This page contains resources and training materials to support OpenRiskNet users in getting familiar with the services and tools available in the e-infrastructure. On top of tutorials and video demonstrations, you will also find information on our publications (e.g. peer-review articles, presentations, posters) that may help you further in learning about OpenRiskNet concepts and implementations.
Case Study report - Toxicogenomics-based prediction and mechanism identification [TGX]
11 Dec 2019
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Abstract:
In this case study a transcriptomics-based hazard prediction model for identification of specific molecular initiating events (MIE) was foreseen based on (A) top-down and (B) bottom-up approaches. The MIEs can include, but are not limited to: (1) Genotoxicity (p53 activation), (2) Oxidative stress (Nrf2 activation), (3) Endoplasmic Reticulum Stress (unfolded protein response), (4) Dioxin-like activity (AhR receptor activation), (5) HIF1 alpha activation and (6) Nuclear receptor activation (e.g. for endocrine disruption). This case study focussed on two top-down approaches for genotoxicity prediction. The first approach resulted in the creation of a Nextflow-based workflow from the publication “A transcriptomics-based in vitro assay for predicting chemical genotoxicity in vivo” by Magkoufopoulou et al. (2012), thereby reproducing their work as proof of principle. The Nextflow-based workflow has been translated into a more generic approach, especially for step 1, forming the basis of the second top-down approach. In this approach transcriptomics data together with toxicological compound information were collected from multiple toxicogenomics studies and used for building a metadata genotoxicity prediction model.
In this case study a transcriptomics-based hazard prediction model for identification of specific molecular initiating events (MIE) was foreseen based on (A) top-down and (B) bottom-up approaches. The MIEs can include, but are not limited to: (1) Genotoxicity (p53 activation), (2) Oxidative stress (Nrf2 activation), (3) Endoplasmic Reticulum Stress (unfolded protein response), (4) Dioxin-like activity (AhR receptor activation), (5) HIF1 alpha activation and (6) Nuclear receptor activation (e.g. for endocrine disruption). This case study focussed on two top-down approaches for genotoxicity prediction. The first approach resulted in the creation of a Nextflow-based workflow from the publication “A transcriptomics-based in vitro assay for predicting chemical genotoxicity in vivo” by Magkoufopoulou et al. (2012), thereby reproducing their work as proof of principle. The Nextflow-based workflow has been translated into a more generic approach, especially for step 1, forming the basis of the second top-down approach. In this approach transcriptomics data together with toxicological compound information were collected from multiple toxicogenomics studies and used for building a metadata genotoxicity prediction model.
Additional materials:
Report
Report
Related services:
Transcriptomics data from human, mouse, rat in vitro liver models
Transcriptomics data from human, mouse, rat in vitro liver models
Target audience: Researchers, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: CRG, UM
OpenRiskNet Part I: Development of an open e-infrastructure predictive toxicology and risk assessment
2 Aug 2019
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Abstract:
OpenRiskNet (https://openrisknet.org/) is a 3-year project funded by the EU within Horizon 2020 EINFRA-22-2016 Programme, with the main objective to develop an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The infrastructure is built on virtual research environments (VREs), which can be deployed to workstations as well as public and in-house cloud infrastructures. Services providing data, data analysis, modelling and simulation tools for risk assessment are integrated into the e-infrastructure and can be combined into workflows using harmonised and interoperable application programming interfaces (APIs) (https://openrisknet.org/e-infrastructure/services/). For complete risk assessment and safe-by-design studies, OpenRiskNet e-infrastructure functionality is combined via a variety of incorporated services demonstrated within a set of case studies (see figure 1). The case studies present real-world settings such as data curation, systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identification of areas of concern based only on alternative methods (non-animal testing) approaches. OpenRiskNet is working with a network of partners, organised within an Associated Partners Programme, aiming to strengthen the working ties to other organisations developing relevant solutions or tools.
OpenRiskNet (https://openrisknet.org/) is a 3-year project funded by the EU within Horizon 2020 EINFRA-22-2016 Programme, with the main objective to develop an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The infrastructure is built on virtual research environments (VREs), which can be deployed to workstations as well as public and in-house cloud infrastructures. Services providing data, data analysis, modelling and simulation tools for risk assessment are integrated into the e-infrastructure and can be combined into workflows using harmonised and interoperable application programming interfaces (APIs) (https://openrisknet.org/e-infrastructure/services/). For complete risk assessment and safe-by-design studies, OpenRiskNet e-infrastructure functionality is combined via a variety of incorporated services demonstrated within a set of case studies (see figure 1). The case studies present real-world settings such as data curation, systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identification of areas of concern based only on alternative methods (non-animal testing) approaches. OpenRiskNet is working with a network of partners, organised within an Associated Partners Programme, aiming to strengthen the working ties to other organisations developing relevant solutions or tools.
Published in: ISMB/ECCB 2019
Target audience: Researchers, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution 4.0 International (CC BY 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Compute and data federation (Deliverable 2.5)
25 Jun 2019
→ doi: 10.5281/zenodo.3256306
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Abstract:
This report details the work involved in the federation of compute and data resources between the OpenRiskNet e-infrastructure and external resources. The reference environment has been designed to be capable of handling the majority of requirements for users’ wishes to deploy and run services. However specific situations demand solutions where either the computation, the data or both reside outside the OpenRiskNet e-infrastructure. This deliverable is related to Tasks 2.7 (Interconnecting virtual environment with external infrastructures) and Tasks 2.8 (Federation between virtual environments). Resource intensive analyses, such as those performed in toxicogenomics, can have CPU, memory or disk requirements that cannot be assumed to be available across all deployment scenarios. Human sequencing data may have restrictions on where it can be processed and the vast quantity of this data often predicates that it is more efficient to “bring the computation to the data”. In achieving Tasks 2.7 and 2.8, we can demonstrate how the virtual environment can utilise external infrastructure including commercial cloud providers and data stores.
This report details the work involved in the federation of compute and data resources between the OpenRiskNet e-infrastructure and external resources. The reference environment has been designed to be capable of handling the majority of requirements for users’ wishes to deploy and run services. However specific situations demand solutions where either the computation, the data or both reside outside the OpenRiskNet e-infrastructure. This deliverable is related to Tasks 2.7 (Interconnecting virtual environment with external infrastructures) and Tasks 2.8 (Federation between virtual environments). Resource intensive analyses, such as those performed in toxicogenomics, can have CPU, memory or disk requirements that cannot be assumed to be available across all deployment scenarios. Human sequencing data may have restrictions on where it can be processed and the vast quantity of this data often predicates that it is more efficient to “bring the computation to the data”. In achieving Tasks 2.7 and 2.8, we can demonstrate how the virtual environment can utilise external infrastructure including commercial cloud providers and data stores.
Target audience: Researchers, Data managers, Data owners, Data modellers, Bioinformaticians, Data providers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, CRG, UM, UU, IM
Use Nextflow for toxicogenomics-based prediction
3 Jun 2019
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Target audience: Researchers, Developers, Data modellers, Bioinformaticians, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: CRG
Use Nextflow for toxicogenomics-based prediction
27 May 2019
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Additional materials:
Slides
Slides
Target audience: Researchers, Students, Developers, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: CRG
OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration and in silico analysis and modelling in risk assessment
10 Oct 2018
→ doi: 10.1016/j.toxlet.2018.06.617
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Abstract:
OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches. We discuss our progress on the OpenRiskNet goal of harmonizing data and metadata within APIs that can be added to already existing analysis and modeling services and data warehouses. We also demonstrate how these APIs can easily be used towards the generation of full risk assessment workflows either using scripting languages or workflow managers. Finally, we show the first approaches to make these APIs semantically rich by annotating data with human- and computer-readable data schemata. OpenRiskNet has initiated the Associated Partners Programme strengthening the working ties between the OpenRiskNet members and other organizations within the scientific community.
OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches. We discuss our progress on the OpenRiskNet goal of harmonizing data and metadata within APIs that can be added to already existing analysis and modeling services and data warehouses. We also demonstrate how these APIs can easily be used towards the generation of full risk assessment workflows either using scripting languages or workflow managers. Finally, we show the first approaches to make these APIs semantically rich by annotating data with human- and computer-readable data schemata. OpenRiskNet has initiated the Associated Partners Programme strengthening the working ties between the OpenRiskNet members and other organizations within the scientific community.
Additional materials:
20180810_ORN_Poster_EuroTox.pdf
20180810_ORN_Poster_EuroTox.pdf
Published in: Toxicology Letters
Publisher: Elsevier
Target audience: Risk assessors, Researchers, Students, OpenRiskNet stakeholders, Regulators, Data modellers, Bioinformaticians
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Approaches for containerized scientific workflows in cloud environments with applications in life science
24 Aug 2018
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Abstract:
Containers are gaining popularity in life science research as they encompass all dependencies of provisioned tools and simplifies software installations for end users, as well as offering a form of isolation between processes. Scientific workflows are ideal to chain containers into data analysis pipelines to sustain reproducible science. In this manuscript we review the different approaches to use containers inside the workflow tools Nextflow, Galaxy, Pachyderm, Luigi, and SciPipe when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.
Containers are gaining popularity in life science research as they encompass all dependencies of provisioned tools and simplifies software installations for end users, as well as offering a form of isolation between processes. Scientific workflows are ideal to chain containers into data analysis pipelines to sustain reproducible science. In this manuscript we review the different approaches to use containers inside the workflow tools Nextflow, Galaxy, Pachyderm, Luigi, and SciPipe when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.
Published in: PeerJ Preprints
Publisher: PeerJ
Target audience: Researchers, Students, Data modellers
Open access: yes
Licence: Attribution 4.0 International (CC BY 4.0)
Organisations involved: CRG, UU
OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration and in silico analysis and modelling in risk assessment
12 Mar 2018
→ doi: 10.5281/zenodo.1199287
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Abstract:
OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches.
OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches.
Publisher: Society of Toxicology (SOT)
Target audience: Risk assessors, Researchers, Regulators, Data modellers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS