Tools & Resources maintained by us
A web-based toolbox and resource for integrative analysis of the human neuronal genome, transcriptome, and more.
An R package and R shiny application for calculating sample size and power of bulk tissue and single-cell eQTL analysis.
One Tip Per Day
Learning notes for R, Unix, Perl, statistics, tools/resources, biology, etc. everything about Bioinformatics
Local mirror of UCSC Genome Browser
Bioinformatics primer articles
- Applied Computational Genomics Course (by Dr. Aaron Quinlan et al.)
- Genomic Data Science Specialization (by Dr. Steven Salzberg et al.)
- Data Analysis for the Life Sciences (by Dr. Rafael Irizarry et al.)
- Genomics Data Analysis (by Dr. Rafael Irizarry et al.)
- Using Python for Research (by Dr. Jukka-Pekka “JP” Onnela)
- PH525x series – Biomedical Data Science (by Rafael Irizarry and Michael Love)
- R for Data Science, by Hadley Wickham & Garrett Grolemund, introduces the key tools for doing data science with R.
- ggplot2: elegant graphics for data analysis, by Hadley Wickham, shows you how to use ggplot2 to create graphics that help you understand your data.
- Advanced R, by Hadley Wickham, helps you master R as a programming language, teaching you what makes R tick.
- R packages, by Hadley Wickham, teaches good software engineering practices for R, using packages for bundling, documenting, and testing your code.
- Data Science at the Command Line, by Jeroen Janssens, demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist.
- Actually, most books on https://bookdown.org
- Genomics in Cloud: Using Docker, GATK, and WDL in Terra, by Brian D. O’Connor @ UCSC and Geraldine A. Van der Auwera @ Broad. Free access with Harvard ID.