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Open AccessArticle

WebSpecmine: A Website for Metabolomics Data Analysis and Mining

1
CEB—Centre Biological Engineering, University of Minho, 4710-057 Braga, Portugal
2
Plant Morphogenesis and Biochemistry Laboratory, Federal University of Santa Catarina, Florianópolis SC 88040-900, Brazil
*
Author to whom correspondence should be addressed.
Metabolites 2019, 9(10), 237; https://doi.org/10.3390/metabo9100237
Received: 8 August 2019 / Revised: 9 October 2019 / Accepted: 15 October 2019 / Published: 19 October 2019
(This article belongs to the Special Issue Recent Advances in Metabolomics (IECM-3))
Metabolomics data analysis is an important task in biomedical research. The available tools do not provide a wide variety of methods and data types, nor ways to store and share data and results generated. Thus, we have developed WebSpecmine to overcome the aforementioned limitations. WebSpecmine is a web-based application designed to perform the analysis of metabolomics data based on spectroscopic and chromatographic techniques (NMR, Infrared, UV-visible, and Raman, and LC/GC-MS) and compound concentrations. Users, even those not possessing programming skills, can access several analysis methods including univariate, unsupervised and supervised multivariate statistical analysis, as well as metabolite identification and pathway analysis, also being able to create accounts to store their data and results, either privately or publicly. The tool’s implementation is based in the R project, including its shiny web-based framework. Webspecmine is freely available, supporting all major browsers. We provide abundant documentation, including tutorials and a user guide with case studies. View Full-Text
Keywords: metabolomics; statistical analysis; data mining; metabolite identification; pathway analysis; open-source software metabolomics; statistical analysis; data mining; metabolite identification; pathway analysis; open-source software
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MDPI and ACS Style

Cardoso, S.; Afonso, T.; Maraschin, M.; Rocha, M. WebSpecmine: A Website for Metabolomics Data Analysis and Mining. Metabolites 2019, 9, 237.

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