Ion Identity Molecular Networking in the GNPS Environment

Abstract

General conceptualization 48 RS, DP, LFN, MW, PCD conceptualized the idea of IIMN and its integration into GNPS and 49 feature-finding software tools 50 RS, DP, LFN, PCD wrote the manuscript 51 RS, BA, FH, HUH conceptualized the MZmine feature grouping workflow 52 UK, HH provided discussion and feedback on IIMN and the MZmine workflow 53 Development 54 RS developed the IIMN modules in MZmine and the MS 2 spectral library generation modules 55 MW, RS developed the "supplementary edges" format in the FBMN workflow to enable IIMN 56 MW programmed the IIMN workflow on GNPS 57 RS, MW developed the direct submission of MZmine data to run IIMN on GNPS 58 JR, MGA developed the XCMS/CAMERA IIMN integration in R 59 HT developed the MS-DIAL FBMN and IIMN integration 60 KD developed the MS 2 spectral merge function into the export modules for FBMN, IIMN, and 61 SIRIUS, which was coordinated by SB 62 TP, AK provided feedback and help for the development and integration of IIMN in MZmine 63

Organic Matter Composition at Ocean Station Papa Affects Its Bioavailability, Bacterioplankton Growth Efficiency and the Responding Taxa

“…A decrease in LC-MS/MS peak area z-scores indicated a decrease in the abundance of those compounds with time. The spectra were submitted to Ion-Identity Molecular Networking (Schmid et al, 2020) in Global Natural Product Social Molecular Networking (GNPS) site to create a molecular network and were then searched against GNPS spectral libraries and National Institute of Standards and Technology Library 17. The approach described here considers the annotated features to be 'putative' identifications that have not yet been verified by reference standards, but are based on spectral similarity to data from public or commercial libraries (Sumner et al, 2007;Longnecker et al, 2015;Longnecker and Kujawinski, 2017).…”

Apicidin Biosynthesis Is Linked to Accessory Chromosomes in Fusarium Poae Isolates

“…Data was imported into the R environment, where mass features representing associated adducts and in-source fragments of the same parent ion were grouped using Pearson correlation analysis over a sliding window of elution time. These groupings were compared to data generated during raw data preprocessing via the Ion Identity Networking module which correlated peak shapes of coeluting signals (47). Mass feature data from the two extraction types (mycelium and broth) were summed, converted to binary form, and then averaged across the ve media conditions to form a 'pseudo-binary' matrix of detection frequencies for each mass feature.…”

“…A: Apicidin (APS) subnetwork generated from feature-based molecular network analysis of APS-like signals using GNPS (release_23) (46), visualized in cytoscape. Nodes represent distinct features (peaks) with unique retention times and m/z, and are either connected by cosine similarity score (threshold = 0.7, blue line) or adduct identity match generated using IIN module (47)…”

Assigning confidence to structural annotations from mass spectra with COSMIC

“…Structural annotation via MS/MS is usually carried out by spectral library search, but annotations are intrinsically restricted to compounds for which a reference spectrum (usually based on commercially available chemicals) is present in the library. Despite ongoing discussions on how many detected features actually correspond to metabolites [6][7][8] , it is widely conjectured that a large fraction of compounds remain uncharacterized [9][10][11] . Beyond establishing a ranking of candidates, the score of the best-scoring candidate in the library (the hit) is used to evaluate the confidence of an annotation: A low hit score indicates that a wrong candidate has been selected, potentially because the correct answer is absent from the library.…”