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Analysis of Metabolomics Datasets with High-Performance Computing and Metabolite Atlases.

Yao Y, Sun T, Wang T, Ruebel O, Northen T, Bowen BP - Metabolites (2015)

Bottom Line: Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly.By using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources.In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.

View Article: PubMed Central - PubMed

Affiliation: National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. yyao@lbl.gov.

ABSTRACT
Even with the widespread use of liquid chromatography mass spectrometry (LC/MS) based metabolomics, there are still a number of challenges facing this promising technique. Many, diverse experimental workflows exist; yet there is a lack of infrastructure and systems for tracking and sharing of information. Here, we describe the Metabolite Atlas framework and interface that provides highly-efficient, web-based access to raw mass spectrometry data in concert with assertions about chemicals detected to help address some of these challenges. This integration, by design, enables experimentalists to explore their raw data, specify and refine features annotations such that they can be leveraged for future experiments. Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly. By using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources. In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.

No MeSH data available.


Authenticated users can acquire data from Metabolite Atlas using IPython and Jupyter notebooks. These notebooks provide a user friendly interface to the Python programming language which contains extensive libraries for data processing including peak fitting as shown here. These notebooks can be easily shared via the nbviewer service [32]. Typical notebooks contain code for analysis, results, and text explaining the purpose of the code.
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getmorefigures.php?uid=PMC4588804&req=5

metabolites-05-00431-f003: Authenticated users can acquire data from Metabolite Atlas using IPython and Jupyter notebooks. These notebooks provide a user friendly interface to the Python programming language which contains extensive libraries for data processing including peak fitting as shown here. These notebooks can be easily shared via the nbviewer service [32]. Typical notebooks contain code for analysis, results, and text explaining the purpose of the code.

Mentions: The framework for computing and analysis is made accessible via a user interface which can capture the steps of an analysis from raw data, statistical analysis, and visualization in transparent and shareable format. Shown in Figure 3 are examples of the Metabolite Atlas accessed through an IPython and Jupyter notebook web interface. The use of these narrative notebooks allows users to share findings and methods through public repositories such as github [30]. As has been shown numerous times, popular methods in social networks will become widespread [31]. These methods will likely reduce the burden on analysis for the degenerate features detected in LC/MS experiments.


Analysis of Metabolomics Datasets with High-Performance Computing and Metabolite Atlases.

Yao Y, Sun T, Wang T, Ruebel O, Northen T, Bowen BP - Metabolites (2015)

Authenticated users can acquire data from Metabolite Atlas using IPython and Jupyter notebooks. These notebooks provide a user friendly interface to the Python programming language which contains extensive libraries for data processing including peak fitting as shown here. These notebooks can be easily shared via the nbviewer service [32]. Typical notebooks contain code for analysis, results, and text explaining the purpose of the code.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588804&req=5

metabolites-05-00431-f003: Authenticated users can acquire data from Metabolite Atlas using IPython and Jupyter notebooks. These notebooks provide a user friendly interface to the Python programming language which contains extensive libraries for data processing including peak fitting as shown here. These notebooks can be easily shared via the nbviewer service [32]. Typical notebooks contain code for analysis, results, and text explaining the purpose of the code.
Mentions: The framework for computing and analysis is made accessible via a user interface which can capture the steps of an analysis from raw data, statistical analysis, and visualization in transparent and shareable format. Shown in Figure 3 are examples of the Metabolite Atlas accessed through an IPython and Jupyter notebook web interface. The use of these narrative notebooks allows users to share findings and methods through public repositories such as github [30]. As has been shown numerous times, popular methods in social networks will become widespread [31]. These methods will likely reduce the burden on analysis for the degenerate features detected in LC/MS experiments.

Bottom Line: Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly.By using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources.In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.

View Article: PubMed Central - PubMed

Affiliation: National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. yyao@lbl.gov.

ABSTRACT
Even with the widespread use of liquid chromatography mass spectrometry (LC/MS) based metabolomics, there are still a number of challenges facing this promising technique. Many, diverse experimental workflows exist; yet there is a lack of infrastructure and systems for tracking and sharing of information. Here, we describe the Metabolite Atlas framework and interface that provides highly-efficient, web-based access to raw mass spectrometry data in concert with assertions about chemicals detected to help address some of these challenges. This integration, by design, enables experimentalists to explore their raw data, specify and refine features annotations such that they can be leveraged for future experiments. Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly. By using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources. In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.

No MeSH data available.