Analysis of metabolomics datasets with high-performance computing and metabolite atlases
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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. Furthermore, 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.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). National Energy Research Scientific Computing Center
- OSTI ID:
- 1342231
- Report Number(s):
- LBNL-1005720; METALU; ir:1005720
- Journal Information:
- Metabolites, Vol. 5, Issue 3; ISSN 2218-1989
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
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