![]() ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics. MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach. Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Automatic recalibration and processing of tandem mass spectra using formula annotation. Towards de novo identification of metabolites by analyzing tandem mass spectra. Accelerated isotope fine structure calculation using pruned transition trees. Loos, M., Gerber, C., Corona, F., Hollender, J. ![]() Valkenborg, D., Mertens, I., Lemière, F., Witters, E. Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, MS/MS fragmentation, heuristic rules, and isotope pattern matching. SIRIUS: decomposing isotope patterns for metabolite identification. Isotopic pattern and accurate mass determination in urine drug screening by liquid chromatography/time-of-flight mass spectrometry. Algorithms in Bioinformatics (WABI 2006) Vol. Decomposing metabolomic isotope patterns. Isotope abundance analysis methods and software for improved sample identification with supersonic gas chromatography/mass spectrometry. METLIN: a technology platform for identifying knowns and unknowns. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Illuminating the dark matter in metabolomics. MassBank: a public repository for sharing mass spectral data for life sciences. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: state of the field and future prospects. Mass spectral reference libraries: an ever-expanding resource for chemical identification. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. Classes for the masses: systematic classification of unknowns using fragmentation spectra. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Evaluation of an artificial neural network retention index model for chemical structure identification in nontargeted metabolomics. Critical assessment of small molecule identification 2016: automated methods. Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. PubChem substance and compound databases. HMDB 4.0: the human metabolome database for 2018. Finally, we report and verify several novel molecular formulas annotated by ZODIAC. We evaluate ZODIAC on five datasets, producing results substantially (up to 16.5-fold) better than for several other methods, including SIRIUS, which is the state-of-the-art algorithm for molecular formula annotation at present. Thorough algorithm engineering ensures fast processing in practice. We employ Bayesian statistics and Gibbs sampling. ![]() Our method re-ranks molecular formula candidates by considering joint fragments and losses between fragmentation trees. This allows us to annotate novel molecular formulas that are absent from even the largest public structure databases. Uniquely, it enables fully automated and swift processing of complete experimental runs, providing high-quality, high-confidence molecular formula annotations. Here we present ZODIAC, a network-based algorithm for the de novo annotation of molecular formulas. This is particularly so for large compounds above 500 daltons, and for de novo annotations, for which we consider all chemically feasible formulas. Yet even the annotation of molecular formulas remains highly challenging. Annotating the molecular formula of a compound is the first step towards its structural elucidation. The confident high-throughput identification of small molecules is one of the most challenging tasks in mass spectrometry-based metabolomics. ![]()
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