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| http://dx.doi.org/10.1016/j.kint.2020.05.022 | DOI Listing |
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Mass spectrometry (MS)-based metabolomics often rely on separation techniques when analyzing complex biological specimens to improve method resolution, metabolome coverage, quantitative performance, and/or unknown identification. However, low sample throughput and complicated data preprocessing procedures remain major barriers to affordable metabolomic studies that are scalable to large populations. Herein, we introduce PeakMeister as a new software tool in the R statistical environment to enable standardized processing of serum metabolomic data acquired by multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), a high-throughput separation platform (<4 min/sample) which takes advantage of a serial injection format of 13 samples within a single analytical run.
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