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The development and application of cutting-edge analytical methods is a core driver for new scientific discoveries, medical diagnostics, and commercial-innovation. Mass spectrometry imaging (MSI) holds the promise for being a transformative technology for advanced studies of metabolic processes with broad applications in life sciences, bioenergy, and health. MSI enables fast assays of microbial metabolism essential in bioengineering problems common to the development of biofuels, drugs, and diagnostics for cancer and other diseases. While the data can be routinely collected, the broad application of MSI 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. Typically individuals that can both develop quality computer software and also understand the biological and technical issues associated with each experiment are required to help even with common, and what ought to be routine analyses. Furthermore, the expert developer is often handicapped due to a lack of standards in data-format, algorithms, and visualization strategies. This is an unfortunate situation in which well-designed experiments have to wait in the queue (often for weeks or month) until someone is available to help with the analysis, in which experiments are not completed due to delayed detection of errors, and in which advanced, large-scale experiments are not performed due to the lack of essential analysis capabilities. Analysis of MSI data is an unsolved big data problem that slows down and prevents high-impact scientific discoveries.







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Researchers should cite this work as follows:

  • (2019), "OpenMSI,"

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