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Metabolites 2015, 5(3), 431-442; doi:10.3390/metabo5030431

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

1
National Energy Research Scientific Computing Center (NERSC) and Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
2
Life Sciences Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Per Bruheim
Received: 13 April 2015 / Revised: 7 July 2015 / Accepted: 13 July 2015 / Published: 20 July 2015
(This article belongs to the Special Issue Bioinformatics and Data Analysis)
View Full-Text   |   Download PDF [3088 KB, uploaded 20 July 2015]   |  

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. View Full-Text
Keywords: SciDB; metabolite atlas; metabolomics; data analysis; IPython; Python; LC/MS; MS/MS; biology SciDB; metabolite atlas; metabolomics; data analysis; IPython; Python; LC/MS; MS/MS; biology
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yao, Y.; Sun, T.; Wang, T.; Ruebel, O.; Northen, T.; Bowen, B.P. Analysis of Metabolomics Datasets with High-Performance Computing and Metabolite Atlases. Metabolites 2015, 5, 431-442.

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