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Interoperable and scalable metabolomics data analysis with microservices
(Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala Sweden)
(European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kigndom)
(Department of Computational Biology, University of Lausanne, Lausanne, Switzerland)
(Department of Neuroscience, Uppsala University, Uppsala, Sweden)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Developing 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 here present a generic method based on microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The method was developed within the PhenoMeNal consortium to support flexible metabolomics data analysis and was designed as a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative data analysis.

Keyword [en]
Bioinformatics, e-infrastructure, microservices, metabolomics, kubernetes, Docker, container
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-331658OAI: oai:DiVA.org:uu-331658DiVA: diva2:1149668
Available from: 2017-10-16 Created: 2017-10-16 Last updated: 2017-10-17
In thesis
1. Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain
Open this publication in new window or tab >>Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Alzheimer’s disease (AD) is a neurodegenerative disease and the major cause of dementia, affecting more than 50 million people worldwide. Chronic pain is long-lasting, persistent pain that affects more than 1.5 billion of the world population. Overlapping and heterogenous symptoms of AD and chronic pain conditions complicate their diagnosis, emphasizing the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms.

To characterize disease pathology of AD, we measured the protein changes in the temporal neocortex region of the brain of AD subjects using mass spectrometry (MS). We found proteins involved in exo-endocytic and extracellular vesicle functions displaying altered levels in the AD brain, potentially resulting in neuronal dysfunction and cell death in AD.

To detect novel biomarkers for AD, we used MS to analyze cerebrospinal fluid (CSF) of AD patients and found decreased levels of eight proteins compared to controls, potentially indicating abnormal activity of complement system in AD.

By integrating new proteomics markers with absolute levels of Aβ42, total tau (t-tau) and p-tau in CSF, we improved the prediction accuracy from 83% to 92% of early diagnosis of AD. We found increased levels of chitinase-3-like protein 1 (CH3L1) and decreased levels of neurosecretory protein VGF (VGF) in AD compared to controls.

By exploring the CSF proteome of neuropathic pain patients before and after successful spinal cord stimulation (SCS) treatment, we found altered levels of twelve proteins, involved in neuroprotection, synaptic plasticity, nociceptive signaling and immune regulation.

To detect biomarkers for diagnosing a chronic pain state known as fibromyalgia (FM), we analyzed the CSF of FM patients using MS. We found altered levels of four proteins, representing novel biomarkers for diagnosing FM. These proteins are involved in inflammatory mechanisms, energy metabolism and neuropeptide signaling.

Finally, to facilitate fast and robust large-scale omics data handling, we developed an e-infrastructure. We demonstrated that the e-infrastructure provides high scalability, flexibility and it can be applied in virtually any fields including proteomics. This thesis demonstrates that proteomics is a promising approach for gaining deeper insight into mechanisms of nervous system disorders and find biomarkers for diagnosis of such diseases.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 82 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1385
Keyword
Bioinformatics, microservices, biomarkers, Alzheimer's disease, chronic pain, fibromyalgia, neuropathic pain, spinal cord stimulation, cloud computing, proteomics, metabolomics, software, workflows, data analysis, mass spectrometry
National Category
Geriatrics Neurology Neurosciences
Research subject
Bioinformatics; Neurology; Geriatrics
Identifiers
urn:nbn:se:uu:diva-331748 (URN)978-91-513-0111-2 (ISBN)
Public defence
2017-12-05, Rosénsalen, Akademiska sjukhuset, Ing 95/96, nbv, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2017-11-14 Created: 2017-10-17 Last updated: 2017-11-14

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