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Abstract Fungi are one of the most diverse groups of organisms with an estimated number of species in the range of 2–3 million. The higher-level ranking of fungi has been discussed in the framework of molecular phylogenetics since Hibbett et al., and the definition and the higher ranks (e.g., phyla) of the ‘true fungi’ have been revised in several subsequent publications. Rapid accumulation of novel genomic data and the advancements in phylogenetics now facilitate a robust and precise foundation for the higher-level classification within the kingdom. This study provides an updated classification of the kingdomFungi, drawing upon a comprehensive phylogenomic analysis ofHolomycota, with which we outline well-supported nodes of the fungal tree and explore more contentious groupings. We accept 19 phyla ofFungi,viz. Aphelidiomycota,Ascomycota,Basidiobolomycota,Basidiomycota,Blastocladiomycota,Calcarisporiellomycota,Chytridiomycota,Entomophthoromycota,Entorrhizomycota,Glomeromycota,Kickxellomycota,Monoblepharomycota,Mortierellomycota,Mucoromycota,Neocallimastigomycota,Olpidiomycota,Rozellomycota,Sanchytriomycota,andZoopagomycota. In the phylogenies,Caulochytriomycotaresides inChytridiomycota; thus, the former is regarded as a synonym of the latter, whileCaulochytriomycetesis viewed as a class inChytridiomycota. We provide a description of each phylum followed by its classes. A new subphylum,SanchytriomycotinaKarpov is introduced as the only subphylum inSanchytriomycota. The subclassPneumocystomycetidaeKirk et al. inPneumocystomycetes,Ascomycotais invalid and thus validated. Placements of fossil fungi in phyla and classes are also discussed, providing examples. 

Abstract microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms’ role in ecology and human health.