mWISE: An Algorithm for Context-Based Annotation of Liquid Chromatography–Mass Spectrometry Features through Diffusion in Graphs

Abstract

Untargeted metabolomics using liquid chromatography coupled to mass spectrometry (LC–MS) allows the detection of thousands of metabolites in biological samples. However, LC–MS data annotation is still considered a major bottleneck in the metabolomics pipeline since only a small fraction of the metabolites present in the sample can be annotated with the required confidence level. Here, we introduce mWISE (metabolomics wise inference of speck entities), an R package for context-based annotation of LC–MS data. The algorithm consists of three main steps aimed at (i) matching mass-to-charge ratio values to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, (ii) clustering and filtering the potential KEGG candidates, and (iii) building a final prioritized list using diffusion in graphs. The algorithm performance is evaluated with three publicly available studies using both positive and negative ionization modes. We have also compared mWISE to other available annotation algorithms in terms of their performance and computation time. In particular, we explored four different configurations for mWISE, and all four of them outperform xMSannotator (a state-of-the-art annotator) in terms of both performance and computation time. Using a diffusion configuration that combines the biological network obtained from the FELLA R package and raw scores, mWISE shows a sensitivity mean (standard deviation) across data sets of 0.63 (0.07), while xMSannotator achieves a sensitivity of 0.55 (0.19). We have also shown that the chemical structures of the compounds proposed by mWISE are closer to the original compounds than those proposed by xMSannotator. Finally, we explore the diffusion prioritization separately, showing its key role in the annotation process. mWISE is freely available on GitHub () under a GPL license.

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

“…Once the dMEs were obtained from the C. obtusifolia metabolome, their annotation and the identification of metabolic pathways enriched by the dMEs were carried out. Python 3.8 and Mummichog 2.3 software were used (Li et al 2013; Python Software Foundation 2020 ; Mummichog 2020 ) to obtain the nomenclature of codes from the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa et al 2016 ) for each dME in both ionization modes (see, for example, Barranco-Altirriba et al 2021 ; Walker et al 2021 ). Arabidopsis thaliana (L.) Heynh.…”

Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis

“…Therefore, the structures are discarded from further consideration or their ranking among the candidate structures is decreased [ 17 ]. In addition, metadata, such as usage and previous detection of the candidate structure, can be used to prioritize some candidate structures over others [ 18 ]. Ideally, after these steps, the detected feature can be annotated with one most probable candidate structure; however, in practice, multiple isomeric candidate structures remain [ 19 ].…”