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The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science. [ABSTRACT FROM AUTHOR]

The identification of smallmolecules from mass spectrometry (MS) data remains amajor challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline [ABSTRACT FROM AUTHOR]

Mycoses induced by C.albicans or A.fumigatus can cause important host damage either by deficient or exaggerated immune response. Regulation of chemokine and cytokine signaling plays a crucial role for an adequate inflammation, which can be modulated by vitamins A and D. Non-coding RNAs (ncRNAs) as transcription factors or cis-acting antisense RNAs are known to be involved in gene regulation. However, the processes during fungal infections and treatment with vitamins in terms of therapeutic impact are unknown. We show that in monocytes both vitamins regulate ncRNAs involved in amino acid metabolism and immune system processes using comprehensive RNA-Seq analyses. Compared to protein-coding genes, fungi and bacteria induced an expression change in relatively few ncRNAs, but with massive fold changes of up to 4000. We defined the landscape of long-ncRNAs (lncRNAs) in response to pathogens and observed variation in the isoforms composition for several lncRNA following infection and vitamin treatment. Most of the involved antisense RNAs are regulated and positively correlated with their sense protein-coding genes. We investigated lncRNAs with stimulus specific immunomodulatory activity as potential marker genes: LINC00595, SBF2-AS1 (A.fumigatus) and RP11-588G21.2, RP11-394l13.1 (C.albicans) might be detectable in the early phase of infection and serve as therapeutic targets in the future. [ABSTRACT FROM AUTHOR]