TurboPutative: A web server for data handling and metabolite classification in untargeted metabolomics - Barrero-Rodríguez, Rafael; Rodriguez, Jose Manuel; Tarifa, Rocío; Vázquez, Jesús; Mastrangelo, Annalaura; Ferrarini, Alessia (Frontiers in Molecular Biosciences, 2022)
Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e. amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger and TableMerger) that streamlines data handling, classification and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves data handling and relevant data visualization allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size facilitating the manual comparison among annotations and the visualization/interpretation of the results. Finally, TableMerger combines different datasets, with relevant information for the researcher, into a unique data matrix facilitating further assessment and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80-90%, thus facilitating the extrapolation of the biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful APIs services. Users can explore the performance of each module through demo datasets supplied in the website. The platform will help the metabolomics community to speed-up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.