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Metabase: A New Programming Framework for Analyzing, Visualizing, and Integrating Multi‐Omics Data for Nutritional Intervention Studies
Author(s) -
Zhu Chenghao,
Zhang Ruihan,
Zivkovic Angela M.
Publication year - 2019
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2019.33.1_supplement.642.10
Subject(s) - omics , computer science , workflow , data science , metabolomics , visualization , field (mathematics) , software , proteomics , bioinformatics , data mining , biology , database , biochemistry , mathematics , pure mathematics , gene , programming language
With the rapid development of sequencing technology and mass spectrometry, high through‐put experimental methods are being adopted into the field of nutritional intervention studies. –Omics methods such as metabolomics, lipidomics, proteomics, and microbiome methods generate large datasets, with high sensitivity, and are powerful to allow researchers to discover subtle metabolic alterations. All –omics methods have their own communities and data analysis workflows that make the integration multi‐omics datasets a challenge for nutritional/clinical researchers. ‐‐R is an open‐source programming language with a large community in the field of data science and bioinformatics. Here we developed a data analysis framework based on the R language, that aims to analyze, visualize, and integrate tabular datasets from different –omics experiments. The Metabase package uses an S4 object‐oriented design. It allows researchers to easily store, manipulate, summarize, and transform –omics data, followed by statistical analysis and visualization. Researchers can adjust their hypothesis for testing and statistical model easily. The Metabase package is inspired by data analysis software from different –omics communities, and is capable of analyzing metabolomic, lipidomic, proteomic, and microbiome data. It is designed with large extensibility and can be extended and customized to handle other experimental data such as anthropometric and dietary data. The Metabase package allows nutritional researchers to analyze and integrate their multi‐omics data and improve reproducibility and data presentation quality. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .