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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...
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A Bioinformatics Primer to Data Science, with Examples for Metabolomics.

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  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

Methods in Molecular Biology (Clifton, N.J.)
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Summary

This guide equips researchers with essential data science skills for handling big biomedical data. It covers computing environments, data management, bioinformatics tasks, and reproducible research practices for career advancement and scientific progress.

Keywords:
BioinformaticsCloud computingData managementData scienceData visualizationMetabolomicsQuality controlScripting

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Area of Science:

  • Bioinformatics and Data Science
  • Biomedical Big Data Analysis

Background:

  • The increasing volume of big data in biomedicine necessitates specialized skills for career development and scientific advancement.
  • Effective data management and analysis are crucial for leveraging high-throughput biomedical data.

Purpose of the Study:

  • To provide a practical guide for working with high-throughput biomedical data.
  • To outline core data science and bioinformatics skills essential for researchers.

Main Methods:

  • Covers setting up computing environments and project management.
  • Details common bioinformatics tasks: data wrangling, quality control, statistical analysis, and visualization using metabolomics data.
  • Discusses coding, scripting, version control, and reproducible research tools like knitr and Jupyter notebooks.

Main Results:

  • Provides a foundational understanding of essential skills for bioinformatics.
  • Demonstrates practical application through metabolomics data examples.
  • Highlights the importance of tools for project management and reproducibility.

Conclusions:

  • This chapter serves as a comprehensive reference for graduate-level bioinformatics and data science training.
  • Equips individuals with the necessary skills to navigate and analyze complex biomedical datasets.
  • Facilitates collaboration and enhances the reproducibility of scientific research in the big data era.