Daniel Cañueto1, Josep Gómez2, Reza M Salek3, Xavier Correig4,5, Nicolau Cañellas6,7. 1. Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain. danielcanueto88@gmail.com. 2. Intensive Care Unit, Joan XXIII University Hospital, IISPV, Carrer Dr. Mallafrè Guasch, 4, 43005, Tarragona, Spain. 3. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, CB10 1SD, UK. 4. Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain. 5. CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, 28029, Madrid, Spain. 6. Metabolomics Platform, IISPV, DEEEA, Universitat Rovira i Virgili, Campus Sescelades, Carretera de Valls, s/n, 43007, Tarragona, Spain. nicolau.canyellas@urv.cat. 7. CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, 28029, Madrid, Spain. nicolau.canyellas@urv.cat.
Abstract
INTRODUCTION: Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. OBJECTIVES: To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. METHODS: rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. RESULTS: The information and quality achieved in two public datasets of complex matrices are maximized. CONCLUSION: rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.
INTRODUCTION: Adoption of automatic profiling tools for 1H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices. OBJECTIVES: To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process. METHODS:rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality. RESULTS: The information and quality achieved in two public datasets of complex matrices are maximized. CONCLUSION:rDolphin is an open-source R package ( http://github.com/danielcanueto/rDolphin ) able to provide the best balance between accuracy, reproducibility and ease of use.
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