- A 78-hr workshop on high-throughput sequencing analysis. We only had 12 hrs; if we had 2 wks we would have taught this: https://github.com/hbctraining/In-depth-NGS-Data-Analysis-Course
- RNA-seqlopedia, an overview of RNA-seq from experimental design through analysis steps.
- Bioconductor's RNA-seq workflow, walking you through a complete example analysis using DESeq2, including PCA plots and heatmaps.
- "Learn Bioinformatics in 100 hours", an adaptation of the Applied Bioinformatics course from Penn State. Costs $25 for the course and the Biostar Handbook. Well worth it if you're interested in learning bioinformatics further.
- Ten simple rules for biologists learning to program, from PLOS Computational Biology
- "Top N Reasons to do a PhD or post-doc in bioinformatics", a blog post on why you would want to do this in the first place.
- Applied Computational Genomics course from University of Utah, taught by Aaron Quinlan (author of BEDTools)
- The Harvard Chan Bioinformatics Core has several fantastic modules: https://github.com/hbctraining/main.
- Introduction to Computational Biology, a course taught by Mike Love (author of DESeq2). Lots of great links in his reading list, as well.
- More in-depth BEDTools tutorial: http://quinlanlab.org/tutorials/bedtools/bedtools.html
- BEDTools documentation: http://bedtools.readthedocs.io/en/latest/index.html
- The workflow we wrote and use for LCDB: https://github.com/lcdb/lcdb-wf. This runs RNA-seq and ChIP-seq analyses in an automated fashion on the biowulf cluster.
- Software Carpentry's The Unix Shell
- Codecademy's Learn the Command Line
- The rather extensive Bash guide
- Environment variables and
$PATH: https://www.datacamp.com/community/blog/environment-variable-data-science
- "swirl", an interactive R tutorial
- Guide to customizing ggplot2 plots
- DataCamp has some R tutorials, and much more in-depth lessons if you pay
- The R Inferno, a tour throught the ugly parts of R told in a format similar to Dante's Inferno
- Bioconductor for Genomic Data Science, materials for a Coursera course. Great intro to the Bioconductor family of R packages.
- DESeq2 tutorial from the authors of DESeq2.
- Textbook, "Broadening Your Statistical Horizons": https://bookdown.org/roback/bookdown-bysh/
- Intro Python: https://realpython.com/
- Learn Python the Hard Way: https://www.learnpythonthehardway.org/
- Nicely-written guide to matplotlib: https://realpython.com/blog/python/python-matplotlib-guide/
- UCSC Genome Browser training page
- Open Helix training videos
- UCSC file formats page, with examples to try