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Metabolites. 2015 Jul 20;5(3):431-42. doi: 10.3390/metabo5030431.

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

Author information

1
National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. yyao@lbl.gov.
2
National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. tsun1215@gmail.com.
3
National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. tony.wang.95@gmail.com.
4
National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. oruebel@lbl.gov.
5
Life Sciences Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. trnorthen@lbl.gov.
6
Life Sciences Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA. bpbowen@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.

KEYWORDS:

IPython; LC/MS; MS/MS; Python; SciDB; biology; data analysis; metabolite atlas; metabolomics

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