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Appendix D — Appendix: Resources for Future Learning

D.1 Resources

This appendix lists web resources that can help you continue building your bioinformatics and data analysis skills after the course. Each site offers a different way to strengthen your understanding, from genomics and R programming to visualization and reproducible analysis.

  • Computational Genomics with R is a practical, book-length resource for learning computational genomics concepts in R. It is especially useful for students who want to connect sequence analysis with data analysis workflows.

  • Plants and Python introduces Python through examples grounded in plant biology and genomics. It is a good starting point for students who want a gentle, biology-centered introduction to programming.

  • Fundamentals of Data Visualization provides clear guidance on making effective scientific figures. It is especially helpful for learning how to choose appropriate plot types and present data honestly and clearly.

  • R for Data Science learning materials collects resources for learning R and data analysis. This page is useful for students who want to continue developing skills in data wrangling, visualization, and statistical thinking.

  • Bioinformatics Data Skills Resources offers tutorials and practical guidance for working with biological data. It is a strong next step for students who want to learn more about file formats, command-line tools, and reproducible analysis.

  • Installing IGV explains how to install the Integrative Genomics Viewer, a widely used tool for exploring genome alignments and annotation tracks. This is a useful reference for students who want to inspect sequencing data visually.

  • Data Science for Librarians, Archivists, and Humanists offers an accessible introduction to data science concepts and workflows. Although it is not specific to biology, it is useful for building general computational confidence and good analytical habits.

  • GATK Workflow Overview provides an overview of a common variant-calling workflow using the Genome Analysis Toolkit. It is a helpful resource for students who want to understand how sequencing reads are processed into biologically interpretable results.

These resources are not required course materials, but they can help you keep learning after the semester ends and give you a broader view of how bioinformatics is practiced in research settings.