MAW - The Reproducible Metabolome Annotation Workflow for Untargeted Tandem Mass Spectrometry
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.17.512224v1?rss=1

Authors: Zulfiqar, M., Gadelha, L., Steinbeck, C., Sorokina, M., Peters, K.

Abstract: Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted Liquid Chromatography Mass Spectrometry (LCMS) 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. We have developed an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data implemented in R and Python to assist the metabolic annotation further by combining Tandem Mass Spectrometry (MS2) input data pre-processing and different computational approaches to give a reliable structure candidate. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound libraries. The libraries are integrated via an R package called Spectra and through the CLI (Command-Line-Interface) version of 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. Following FAIR (Findable, Accessible, Interoperable, Reusable) principles, MAW is available as Docker images. The source code and documentation are available on the GitHub repository zmahnoor14/MAW and the version of the workflow presented in this article has a DOI (10.5281/zenodo.7148450) associated with it.

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