MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Abstract

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Untargeted Metabolomics to Expand the Chemical Space of the Marine DiatomSkeletonema marinoi

“…The Metabolome Annotation Workflow (MAW) was used to perform dereplication using spectral databases (GNPS [26], Massbank [27], and HMDB [28, 29]), and compound databases integrated with SIRIUS4 [30, 31]. The results were post-processed and each distinct feature was assigned a list of putative structures with Metabolomics Standards Initiative (MSI) confidence levels of identification [22, 32, 33]. Table 1 shows the number of unique features annotated within each MSI level.…”

“…Here, we performed the metabolome annotation of the mass spectrometry data obtained from S. marinoi to comprehend the chemical composition of the primary and specialised metabolites produced by the diatom. We annotated molecular structures, chemical classes, and molecular formulae to MS features obtained with High-resolution Liquid Chromatography-Electrospray Ionization-Tandem Mass Spectrometry (LC-ESI-Orbitrap MS 2 ) using Metabolome Annotation Workflow (MAW) [22]. The findings of this study contribute to the expansion of known marine natural products derived from S. marinoi .…”

Analysis of Metabolomics and Transcriptomics Data to Assess Interactions in Microalgal Co-culture ofSkeletonema marinoiandPrymnesium parvum

“…The MS 2 fragmentation spectra were acquired in data-dependent acquisition (DDA) mode for the precursor masses ( m / z ) in the inclusion list of selected high-intensity peaks from MS 1 spectra. MAW (22) was used to annotate chemical structures from spectral databases and the COCONUT database (23) to the MS 2 spectra. The number of features annotated within each MSI level is given in Table 2.…”

“…The chemical structure annotation was performed using the Metabolome Annotation Workflow (MAW) (22). All four conditions (endo_pos, endo_neg, exo_pos, and exo_neg) were combined into four .mzML input files for MAW.…”

NanoLC-EI-MS: Perspectives in Biochemical Analysis

“…Despite the availability of different fragmentation techniques, collision-induced dissociation (CID) remains the primary method used in MS/MS. However, CID spectra exhibit limited reproducibility between instruments, making them unsuitable for the automated identification of analytes through spectra comparison [15,16]. As a result, the confident annotation of organic compounds in LC-MS untargeted studies using ESI-MS/MS remains a significant challenge, even with the development of advanced computational tools like molecular networks [17,18].…”