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- Emami Khoonsari, Payam, et al.
(författare)
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Interoperable and scalable data analysis with microservices : Applications in metabolomics
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 35:19, s. 3752-3760
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Tidskriftsartikel (refereegranskat)abstract
- MotivationDeveloping a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator.ResultsWe developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.
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| 2. |
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| 3. |
- Moreno, Pablo, et al.
(författare)
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Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator.
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| 4. |
- Sha, Bo, et al.
(författare)
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Exploring open cheminformatics approaches for categorizing per- and polyfluoroalkyl substances (PFASs)
- 2019
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Ingår i: Environmental Science. - : Royal Society of Chemistry (RSC). - 2050-7887 .- 2050-7895. ; 21:11, s. 1835-1851
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Tidskriftsartikel (refereegranskat)abstract
- Per- and polyfluoroalkyl substances (PFASs) are a large and diverse class of chemicals of great interest due to their wide commercial applicability, as well as increasing public concern regarding their adverse impacts. A common terminology for PFASs was recommended in 2011, including broad categorization and detailed naming for many PFASs with rather simple molecular structures. Recent advancements in chemical analysis have enabled identification of a wide variety of PFASs that are not covered by this common terminology. The resulting inconsistency in categorizing and naming of PFASs is preventing efficient assimilation of reported information. This article explores how a combination of expert knowledge and cheminformatics approaches could help address this challenge in a systematic manner. First, the splitPFAS approach was developed to systematically subdivide PFASs (for eventual categorization) following a CnF2n+1-X-R pattern into their various parts, with a particular focus on 4 PFAS categories where X is CO, SO2, CH2 and CH2CH2. Then, the open, ontology-based ClassyFire approach was tested for potential applicability to categorizing and naming PFASs using five scenarios of original and simplified structures based on the splitPFAS output. This workflow was applied to a set of 770 PFASs from the latest OECD PFAS list. While splitPFAS categorized PFASs as intended, the ClassyFire results were mixed. These results reveal that open cheminformatics approaches have the potential to assist in categorizing PFASs in a consistent manner, while much development is needed for future systematic naming of PFASs. The splitPFAS tool and related code are publicly available, and include options to extend this proof-of-concept to encompass further PFASs in the future.
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