MoleculeNetworking

require(mzkit); #' Molecular Networking (MN) is a computational strategy that may help visualization and interpretation of the complex data arising from MS analysis. imports "MoleculeNetworking" from "mzDIA";

Molecular Networking (MN) is a computational strategy that may help visualization and interpretation of the complex data arising from MS analysis.

MN is able to identify potential similarities among all MS/MS spectra within the dataset and to propagate annotation to unknown but related molecules (Wang et al., 2016). This approach exploits the assumption that structurally related molecules produce similar fragmentation patterns, and therefore they should be related within a network (Quinn et al., 2017). In MN, MS/MS data are represented in a graphical form, where each node represents an ion with an associated fragmentation spectrum; the links among the nodes indicate similarities of the spectra. By propagation of the structural information within the network, unknown but structurally related molecules can be highlighted and successful dereplication can be obtained (Yang et al., 2013); this may be particularly useful for metabolite and NPS identification. MN has been implemented In different fields, particularly metabolomics And drug discovery (Quinn et al., 2017); MN In forensic toxicology was previously used by Allard et al. (2019) For the retrospective analysis Of routine cases involving biological sample analysis. Yu et al. (2019) also used MN analysis For the detection Of designer drugs such As NBOMe derivatives And they showed that unknown compounds could be recognized As NBOMe-related substances by MN. In the present work the Global Natural Products Social platform (GNPS) was exploited to analyze HRMS/MS data obtained from the analysis of seizures collected by the Italian Department of Scientific Investigation of Carabinieri (RIS). The potential of MN to highlight And support the identification of unknown NPS belonging to chemical classes such as fentanyls And synthetic cannabinoids has been demonstrated.

.NET clr function exports
as.data.frame.rawpeakassign	RawPeakAssign:
uniqueNames	makes the spectrum data its unique id reference uniqued!
as.graph	convert the cluster tree into the graph model
tree	do spectrum data clustering
splitClusterRT	Split each cluster data into multiple parts by a givne rt window
clustering	Do spectrum clustering on a small bundle of the ms2 spectrum from a single raw data file
msBin	populate a list of peak ms2 cluster data
representative	create representative spectrum data
spectrum_clusters	get all aligned spectrum clusters across rawdata files
spectrum_grid	Create grid clustering of the ms2 spectrum data
grid_assigned	Make precursor assigned to the cluster node
unpack_unmapped
unpack_assign	Unpack of the spectrum data into multiple file groups

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