These are resources to help students interested in developing foundational skills useful for computational biology. The basic section contains short introductions, and advanced resources are more complete references.

Basic resources and introductions

1. Git and GitHub. Git can feel overwhelming, but just dive in and it will be worth your time.
   • A minimal tutorial by K Broman
   • A gentle introduction to git
   • Git basics from ProGit
2. Markdown. Markdown is an easy-to-read, easy-to-write plain text format.
   • Markdown Tutorial
   • Mastering Markdown
   • Advantages of Document Markup Languages vs WYSIWYG Editors
3. R and Bioconductor. R is unparalleled for statistical packages and powerful data visualization.
   • Learn R Programming intro to R.
   • Bioconductor for Genomic Data Science (Watch the first 4 lectures, about 25 minutes of video)
   • Swirl Interactive platform for learning R
4. Python. Python is a nice general-purpose programming language
   • Dive into python - a free online textbook.
5. Unix command line.
   • Matt Might’s basic guide to Unix
6. YAML
   • Learn YAML in 5 minutes

Advanced resources and complete references

1. Git and GitHub
   • Resources for learning git
   • Setting up authorized SSH keys with github
   • Writing good commit messages (Read after you’ve used git for awhile)
   • ProGit
   • ProGit on version control
   • GitHub Cheatsheet
2. Markdown
   • Markdown Original Specification (and that site in markdown)
   • Github Flavored Markdown
   • Markdown Cheatsheet
3. R
   • An introduction to R - all-purpose intro from the R Foundation
   • Advanced R - Wickam’s free online book on R in general
   • data.table cheat sheet
   • Matt Dowle’s data.table talk - describes motivation for creating data.table.
   • R cookbook - ggplot recipes
   • Coursera R Programming course - R programming course including instructional videos
   • ggplot2 cheatsheet
4. Bioconductor
   • Kasper Hansen’s course, Bioconductor for Genomic Data Science (about 6 hours of video)
   • Reference card
   • Stuff from Laurent Gatto
   • Website and material for CSAMA; for example, you can identify online course material at bioconductor’s course listing; some example courses: Intro to bioconductor, Machine Learning and Parallel Computing, RNA-seq
   • Vignettes from GenomicRanges
   • Vignettes from LOLA
5. Unix command line
   • Bash guide for beginners
   • Bash cheatsheet
   • Matt Might’s scupting text
   • Matt Might’s settling in to unix
   • Search for Unix command line tutorials
6. Python
   • How to Think Like a Computer Scientist: Learning with Python
   • Pandas cheatsheet
7. Programming concepts
   • Software carpentry
   • Coursera
   • Stanford CS107 Programming Paradigms
8. High-performance computing and SLURM. SLURM (the Simple Linux Utility for Resource Management) is the cluster workload manager used by Rivanna at UVA and at many high-performance clusters elsewhere. If you need to submit jobs to a cluster, learn SLURM.
   • Introduction to Rivanna at UVA
   • SLURM Quick Start Guide
   • SLURM docs at Rivanna
   • Rivanna environment modules gives you commands to load installed software into your linux environment
9. Visualization
   • Fundamentals of Data Visualization - online book covering visualization elements and problem categories
10. Writing
    • Duke Scientific Writing Resource
11. Writing R/Bioconductor packages
    • Writing R packages - Wickam’s free online book on writing R packages
    • Blog post on writing R packages - A gentle introduction to your first R package.
    • Roxygen2 is the generally accepted method for documenting R packages
    • Bioconductor Contributions
    • BioC HowTo
12. Workshops
    • For Bioinformatics workshops at UVA check out Bioconnector.