Abstract
With the increasing importance of big data in biomedicine, skills in data science are a foundation for the individual career development and for the progress of science. This chapter is a practical guide to working with high-throughput biomedical data. It covers how to understand and set up the computing environment, to start a research project with proper and effective data management, and to perform common bioinformatics tasks such as data wrangling, quality control, statistical analysis, and visualization, with examples on metabolomics data. Concepts and tools related to coding and scripting are discussed. Version control, knitr and Jupyter notebooks are important to project management, collaboration, and research reproducibility. Overall, this chapter describes a core set of skills to work in bioinformatics, and can serve as a reference text at the level of a graduate course and interfacing with data science.
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Acknowledgments
This work has been funded, in part, by the US national Institutes of Health via grants UH2 AI132345 (Li), U2C ES030163 (Jones, Li, Morgan, Miller), U01 CA235493 (Li, Xia, Siuzdak), U2C ES026560 (Miller), P30 ES019776 (Marsit), P50 ES026071 (McCauley), and the US EPA grant 83615301 (McCauley).
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Pittard, W.S., Villaveces, C.“., Li, S. (2020). A Bioinformatics Primer to Data Science, with Examples for Metabolomics. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_14
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DOI: https://doi.org/10.1007/978-1-0716-0239-3_14
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Publisher Name: Humana, New York, NY
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