Computational Biology Gists

Computational Biology & Bioinformatics Resources

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Computational Biology & Bioinformatics Resources

With programming resources on R, Python, Unix, Git, and Stats. Other non-compbio gists will be here!

NOTE: When the recommendation is an online course, we recommend the FREE version.

Contributors

Janani Ravi & Arjun Krishnan

NOTE: You can request gist on a particular topic by adding an issue outlining the details of the problem. Keywords of interest are in the repo description above.

Table of Contents

• Cheatsheets
• Unix
• R
• Python
• Probability & Statistics
• Biology

Cheatsheets

For R/RStudio, Git/GitHub, Markdown, Unix/vi, Slack, …
https://github.com/jananiravi/cheatsheets

Unix

• Command-line Bootcamp

• Command-line Guide

  Also interactive, just like the bootcamp.

• Linux Journey
• A Unix workshop: course materials
  • Day1 - Video & Slides
  • Day2 - Video & Slides
  • Day3 - Video & Slides
• Command-line refresher from Software Carpentry

R

General introduction to R

• Swirl (‘R Programming’ & ‘Data Analysis’ lessons)
• Programming with R
• RStudio Education

• Finding Your Way To R

  Beginners

• RStudio Essentials
• R Cheatsheets

Data Visualization

A few useful resources to share along with the tidyverse/ggplot

1. To pick the right kind of visualization, given your data type: https://www.data-to-viz.com/
2. Graph galleries w/ sample codes for R/python-newbies:
   R Graph Gallery | Python Graph Gallery

3. ggplot extension gallery

   https://github.com/ggplot2-exts/gallery

R for data science and machine learning

• Data Science Course in a Box - Introductory data science course covering data acquisition and wrangling, exploratory data analysis, data visualization, inference, modeling, and effective communication of results (with tidyverse, R Markdown, and version control). The course also introduces interactive visualization and reporting, text analysis, and Bayesian inference.

• [RStudio

  The Essentials of Data Science](https://resources.rstudio.com/the-essentials-of-data-science)

• R for Reproducible Scientific Analysis

eBooks for R

• R for Data Science | R4DS | Hadley Wickham, Garrett Grolemund | eBook
• Hands-On Programming with R | HOPR | Garrett Grolemund | eBook
• Happy Git and GitHub for the useR | Jenny Bryan | eBook
• Learning Statistics with R | Danielle Navarro | eBook

• Computational Genomics with R	Altuna Akalin
  eBook	Work in progress

• R Programming for Data Science | Roger Peng | eBook
• R Graphics Cookbook | Winston Chang | eBook

Python

General introduction to Python

• Learning Python the Hard Way
• Google Python Class
  • Videos to follow along
• Introduction to Interactive Programming in Python
  • Part 1
  • Part 2

Python for data science and machine learning

• Courses to learn introductory computer science, programming, computational thinking, and data science (video lectures + notes + assignments):
  • Introduction to Computer Science and Programming in Python
  • Introduction to Computational Thinking and Data Science
  • A Whirlwind Tour of Python: PDF and Jupyter Notebooks
  • Scipy Lecture Notes – Awesome document to learn numerics, science, and data with Python
• Data Wrangling:
  • Data Wrangling in Python with Pandas - Kaggle
  • Video series on data analysis with Pandas – Excellent set of short videos
• Visualization:
  • Data Visualization with Python - Kaggle
  • Python Plotting for Exploratory Data Analysis
• Machine Learning:
  • Introduction to ML in Python - Kaggle (Checkout both Levels 1 & 2)
  • Another intro to ML with scikit-learn – This one contains videos and accompanying JuPyter notebooks + blog posts.
  • A Quick Demo to ML with Scikit Learn Python Package – A nice demo+tour of scikit learn.
  • Deep Learning with Python and TensorFlow - Kaggle
  • Embeddings with Python and TensorFlow - Kaggle – Build deep learning models that handle sparse categorical variables
  • Machine Learning Explainability
• General mutli-topic resources:
  • A Step-by-step Guide to Python for Data Science
  • Always checkout the latest PyCon Conference tutorials and talks, almost all of which are available online. For e.g., here’s a list from PyCon 2017.

Probability and statistics

• Think Stats (book + code + solutions; for Python programmers).
• Learning statistics with R (book + code + solutions; for R programmers).
• Points of Significance - an awesome collection of short articles on a variety of topics in statistical data analysis.
• OpenIntro to Probablity and Statistics

Statistical learning

A great resource (book + videos + slides + exercises + example code + solutions) for simultaneously learning both statistical learning and R. [Statistical learning is just another term for machine learning done from a slightly statistical-modeling point-of-view.]

• An Introduction to Statistical Learning with Applications in R | Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani http://www-bcf.usc.edu/~gareth/ISL/index.html
  • You can download the latest version of the book as a PDF on that site: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
  • I would encourage watching these excellent course lecture videos (by the authors, who’re world-class scientists) that follow the book closely: http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/
  • There are additional slides & videos from another good course taught based on this book: https://www.alsharif.info/iom530

Biology

• Learn genetics
• IBiology
• DNA seen through the eyes of a coder - If you have a computational/quantitaive background, you’ll esp. love this!