Limits...
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.


User interface for adjusting the retention time bounds. Integrated access to raw LC/MS data and a Metabolite Atlas is used to adjust retention time bounds. As improved retention and m/z bounds are specified the parameters for each compound are automatically updated in a Metabolite Atlas.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4588804&req=5

metabolites-05-00431-f002: User interface for adjusting the retention time bounds. Integrated access to raw LC/MS data and a Metabolite Atlas is used to adjust retention time bounds. As improved retention and m/z bounds are specified the parameters for each compound are automatically updated in a Metabolite Atlas.

Mentions: Selection of appropriate retention times is a critical and often time-consuming process. To facilitate this process Metabolite Atlas has a user interface enabling direct adjustment to retention time bounds as shown in Figure 2, for the example of nicotinamide. In this case, the retention time bounds for nicotinamide are observed to not precisely conform to the actual measured retention time characteristics of the measured chromatogram for nicotinamide. Based on this observation, the user updates their Atlas for nicotinamide and the results are automatically updated in the Metabolite Atlas.


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)

User interface for adjusting the retention time bounds. Integrated access to raw LC/MS data and a Metabolite Atlas is used to adjust retention time bounds. As improved retention and m/z bounds are specified the parameters for each compound are automatically updated in a Metabolite Atlas.
© Copyright Policy
Related In: Results  -  Collection

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

metabolites-05-00431-f002: User interface for adjusting the retention time bounds. Integrated access to raw LC/MS data and a Metabolite Atlas is used to adjust retention time bounds. As improved retention and m/z bounds are specified the parameters for each compound are automatically updated in a Metabolite Atlas.
Mentions: Selection of appropriate retention times is a critical and often time-consuming process. To facilitate this process Metabolite Atlas has a user interface enabling direct adjustment to retention time bounds as shown in Figure 2, for the example of nicotinamide. In this case, the retention time bounds for nicotinamide are observed to not precisely conform to the actual measured retention time characteristics of the measured chromatogram for nicotinamide. Based on this observation, the user updates their Atlas for nicotinamide and the results are automatically updated in the Metabolite Atlas.

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.