MOTIVATION: The use of stable isotope labeling is highly advantageous for structure elucidation in metabolomics studies. However, computational tools dealing with multiple-precursor-based labeling studies are still missing. Hence, we developed Miso, an R package providing automated and efficient data analysis workflow to detect the complete repertoire of labeled molecules from multiple-precursor-based labeling experiments. RESULTS: The capability of Miso is demonstrated by the analysis of liquid chromatography-mass spectrometry data obtained from duckweed plants fed with one unlabeled and two differently labeled tyrosine (unlabeled tyrosine, tyrosine-2H4 and tyrosine-13C915N1). The resulting data matrix generated by Miso contains sets of unlabeled and labeled ions with their retention time, m/z values and number of labeled atoms that can be directly utilized for database query and biological studies. AVAILABILITY AND IMPLEMENTATION: Miso is publicly available on the CRAN repository (https://cran.r-project.org/web/packages/Miso). A reproducible case study and a detailed tutorial are available from GitHub (https://github.com/YonghuiDong/Miso_example). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Revealing the subcellular localization of proteins within membrane-bound compartments is of a major importance for inferring protein function. Though current high-throughput localization experiments provide valuable data, they are costly and time-consuming, and due to technical difficulties not readily applicable for many Eukaryotes. Physical characteristics of proteins, such as sequence targeting signals and amino acid composition are commonly used to predict subcellular localizations using computational approaches. Recently it was shown that protein-protein interaction (PPI) networks can be used to significantly improve the prediction accuracy of protein subcellular localization. However, as high-throughput PPI data depend on costly high-throughput experiments and are currently available for only a few organisms, the scope of such methods is yet limited. RESULTS: This study presents a novel constraint-based method for predicting subcellular localization of enzymes based on their embedding metabolic network, relying on a parsimony principle of a minimal number of cross-membrane metabolite transporters. In a cross-validation test of predicting known subcellular localization of yeast enzymes, the method is shown to be markedly robust, providing accurate localization predictions even when only 20% of the known enzyme localizations are given as input. It is shown to outperform pathway enrichment-based methods both in terms of prediction accuracy and in its ability to predict the subcellular localization of entire metabolic pathways when no a-priori pathway-specific localization data is available (and hence enrichment methods are bound to fail). With the number of available metabolic networks already reaching more than 600 and growing fast, the new method may significantly contribute to the identification of enzyme localizations in many different organisms.