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
- Git and GitHub. Git can feel overwhelming, but just dive in and it will be worth your time.
- Markdown. Markdown is an easy-to-read, easy-to-write plain text format.
- R and Bioconductor. R is unparalleled for statistical packages and powerful data visualization.
- Unix command line.
Advanced resources and complete references
- Git and GitHub
- 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
- Unix command line
- How to Think Like a Computer Scientist: Learning with Python
- Dive into python - a free online textbook.
- Programming concepts
- 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.
- Duke Scientific Writing Resource
- Writing R/Bioconductor packages