scholarly journals Interoperable and scalable data analysis with microservices: applications in metabolomics

2019 ◽  
Vol 35 (19) ◽  
pp. 3752-3760 ◽  
Author(s):  
Payam Emami Khoonsari ◽  
Pablo Moreno ◽  
Sven Bergmann ◽  
Joachim Burman ◽  
Marco Capuccini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Large Scale ◽  
Scientific Discipline ◽  
Supplementary Information ◽  
Resonance Spectroscopy ◽  
Research Environment ◽  
Metabolomics Data ◽  
Analysis Workflow ◽  
Virtual Research Environment

Abstract Motivation 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 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. Results We 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. Availability and implementation The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Interoperable and scalable data analysis with microservices: Applications in Metabolomics

2017 ◽  
Author(s):  
Payam Emami Khoonsari ◽  
Pablo Moreno ◽  
Sven Bergmann ◽  
Joachim Burman ◽  
Marco Capuccini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Large Scale ◽  
Metabolite Identification ◽  
Access Point ◽  
Scientific Discipline ◽  
Resonance Spectroscopy ◽  
Magnetic Resonance Spectroscopy Study ◽  
Analysis Workflow ◽  
Computational Resources

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 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 in parallel using the Kubernetes container orchestrator. The access point is 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 in the field of metabolomics 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 science.

Miso: an R package for multiple isotope labeling assisted metabolomics data analysis

2019 ◽  
Vol 35 (18) ◽  
pp. 3524-3526 ◽  
Author(s):  
Yonghui Dong ◽  
Liron Feldberg ◽  
Asaph Aharoni
Keyword(s):  
Data Analysis ◽  
Isotope Labeling ◽  
R Package ◽  
Mass Spectrometry Data ◽  
Data Matrix ◽  
Supplementary Information ◽  
Metabolomics Data ◽  
Biological Studies ◽  
Analysis Workflow ◽  
Efficient Data

Abstract Motivation The use of stable isotope labeling is highly advantageous for structure elucidation in metabolomics studies. However, computational tools dealing with multiple-precursor-based labeling studies are still missing. Hence, we developed Miso, an R package providing automated and efficient data analysis workflow to detect the complete repertoire of labeled molecules from multiple-precursor-based labeling experiments. Results The capability of Miso is demonstrated by the analysis of liquid chromatography-mass spectrometry data obtained from duckweed plants fed with one unlabeled and two differently labeled tyrosine (unlabeled tyrosine, tyrosine-2H4 and tyrosine-13C915N1). The resulting data matrix generated by Miso contains sets of unlabeled and labeled ions with their retention time, m/z values and number of labeled atoms that can be directly utilized for database query and biological studies. Availability and implementation Miso is publicly available on the CRAN repository (https://cran.r-project.org/web/packages/Miso). A reproducible case study and a detailed tutorial are available from GitHub (https://github.com/YonghuiDong/Miso_example). Supplementary information Supplementary data are available at Bioinformatics online.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Old habits, new dynamics: a case study of Web 2.0 in collaborative humanities and arts research

2011 ◽  
Vol 5 (2) ◽  
pp. 147-157
Author(s):  
Dan Goren
Keyword(s):  
Web 2.0 ◽  
Digital Technology ◽  
Large Scale ◽  
Web 2.0 Technologies ◽  
Research Project ◽  
Research Practices ◽  
Research Environment ◽  
Arts And Humanities ◽  
Virtual Research Environment

Whilst the application of online multimedia digital technology within arts and humanities research has burgeoned over the last decade, the practice of openly conducting collaborative and in particular discursive research publicly online remains one of the most unfamiliar and conceptually problematic areas for many academics in the field. Based on user surveys, blog posts, and forum discussions, this article provides both an account and assessment of Web 2.0 technologies in use on a large-scale arts and humanities research project. Examining usage by and impressions of both the project team and the wider community of users, it investigates both the advantages gained and problems faced through the use of a virtual research environment (VRE). It also pays special attention to the use of video and its implications for research practices.

scholarly journals MetumpX—a metabolomics support package for untargeted mass spectrometry

2019 ◽  
Vol 36 (5) ◽  
pp. 1647-1648 ◽  
Author(s):  
Bilal Wajid ◽  
Hasan Iqbal ◽  
Momina Jamil ◽  
Hafsa Rafique ◽  
Faria Anwar
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Small Molecules ◽  
Software Package ◽  
Life Sciences ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Software Packages ◽  
Develop Software ◽  
User Friendly

Abstract Motivation Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. Results MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations. Availability and implementation https://github.com/hasaniqbal777/MetumpX-bin. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An automated proteomic data analysis workflow for mass spectrometry

2009 ◽  
Vol 10 (Suppl 11) ◽  
pp. S17 ◽  
Author(s):  
Ken Pendarvis ◽  
Ranjit Kumar ◽  
Shane C Burgess ◽  
Bindu Nanduri
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Proteomic Data ◽  
Analysis Workflow
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