---+!! Learn Biocomputing %TOC% ---++ General Resources *Software Carpentry* %BR% https://software-carpentry.org/lessons/ *Code Academy* %BR% https://www.codecademy.com/ ---++ Learning R The programming language R was originally designed for statistical analysis, but researchers now use it for a wide variety of data analysis and visualization tasks. %BR% Download R here: https://www.r-project.org %BR% Use the RStudio IDE to write and run R code. Download here: https://rstudio.com *Primers on RStudio Cloud* %BR% https://rstudio.cloud/learn/primers %BR% These are short interactive lessons that let you write R code in your web browser. They use the "tidyverse" style of R coding, which we recommend for all new users. The first 4 lessons: "Basics", "Work with Data", "Visualize Data", and "Tidy your Data" give a strong foundation for writing R code to explore your data. *Primers on data analysis/visualization in R* %BR% https://wilkelab.org/classes/SDS348_spring_2020.html (first half of lectures) ---++ Learning Python (for biology) Python has become a dominant programming language for asking and answering many different questions in biology. This is because of its relative simplicity yet powerful flexibility, and also because Python has extensive library support for various biological tools. *Python Installation* %BR% There are many different ways to run Python, but one of the easiest choices to get started is [[https://colab.research.google.com/][Google Colabroratory]]. This is essentially an online [[https://jupyter.org/][Jupyter Notebook]], which has many popular packages pre-installed, and removes much of the hassle that can come from a local intallation of python. [[https://jupyter.org/][Jupyter Notebooks]] are a great tool for keeping track of what the user has tried, and also seamlessly integrating spaces where the user can describe their thought process or add other notes. It is conceptually similar to a lab notebook that one might use in a wet lab. If a local installation of Python is prefered, [[https://www.anaconda.com/][Anaconda]] is a great choice. Anaconda is a data science platform that installs many useful tools, including Jupyter Notebook, and allows for easier package management. Packages contain libraries of useful code that other people have written making Python easier to use. [[https://biopython.org/][Biopython]] is one such such library, and contains indespensible tools such as FASTA/GBK parsers/writers, and tools for manipulating DNA/protein sequences. *Classes/resources* %BR% [[https://www.codecademy.com/][Codecademy]] offers short lessons that teach the basics of Python [[https://en.wikipedia.org/wiki/Syntax_(programming_languages)][syntax]]. Professor Claus Wilke of The University of Texas at Austin has [[https://wilkelab.org/classes/SDS348_spring_2020.html][a great class posted online]] (second half of lectures) that introduces how to use Python in a biological context. ---++Git / GitHub *Interactive Git lesson* %BR% https://github.com/jlord/git-it-electron ---++ Recommended Books *Practical Computing for Biologists* by Steven Haddock and Casey Dunn %BR% [[https://www.amazon.com/dp/0878933913/ref=cm_sw_r_tw_dp_U_x_5WVPEbFH8JJ6V][Buy on Amazon]] %BR% This book provides an exceptionally clean and well written introduction for biologists to begin working with computational data. The beginning focus on regular expressions and working with text files will help anyone starting out with coding. %BR% %BR% *Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools* by Vince Buffalo %BR% [[https://www.amazon.com/dp/0878933913/ref=cm_sw_r_tw_dp_U_x_5WVPEbFH8JJ6V][Buy on Amazon]] %BR% This book is one of the few available "intermediate" level bioinformatic texts. Once you have mastered some basics in UNIX or Python/R programming, this books introduces more advanced concepts like workflow management and dealing with specific biological data.
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Topic revision: r3 - 2020-04-27 - MattMcGuffie