ArticleJournal of the American Society for Mass Spectrometry2024
Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data.
Article in Journal of the American Society for Mass Spectrometry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of
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