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Title: BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI

Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. Here, we demonstrate the application of BASTet and OpenMSI inmore » practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.« less
Authors:
 [1] ;  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Visualization and Computer Graphics
Additional Journal Information:
Journal Volume: 24; Journal Issue: 1; Related Information: © 1995-2012 IEEE.; Journal ID: ISSN 1077-2626
Publisher:
IEEE
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mass spectrometry imaging; Data provenance; Visualization; Data management; Analysis Workflows; Data sharing
OSTI Identifier:
1465426

Rubel, Oliver, and Bowen, Benjamin P. BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI. United States: N. p., Web. doi:10.1109/TVCG.2017.2744479.
Rubel, Oliver, & Bowen, Benjamin P. BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI. United States. doi:10.1109/TVCG.2017.2744479.
Rubel, Oliver, and Bowen, Benjamin P. 2017. "BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI". United States. doi:10.1109/TVCG.2017.2744479. https://www.osti.gov/servlets/purl/1465426.
@article{osti_1465426,
title = {BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI},
author = {Rubel, Oliver and Bowen, Benjamin P.},
abstractNote = {Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. Here, we demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.},
doi = {10.1109/TVCG.2017.2744479},
journal = {IEEE Transactions on Visualization and Computer Graphics},
number = 1,
volume = 24,
place = {United States},
year = {2017},
month = {8}
}