Read more about A Primer for Computational Biology

A Primer for Computational Biology

(2 reviews)

Shawn T. O'Neil, Oregon State University

Copyright Year: 2017

Publisher: Oregon State University

Language: English

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Reviewed by Ford Fishman, Associate Instructor, Indiana University - Bloomington on 5/17/21

This text does an excellent job covering the basics of Unix, Python, and R. It goes through each and explains all of the foundational approaches in an easily understandable manner. The online index is also quite effective and takes you to the... read more

Reviewed by Herbert Sizek, Graduate Student in Informatics and Biology, Indiana University - Bloomington on 5/17/21

This book covers the basics of operating within a Unix environment, whether local or remote, and programing in Python and R, focusing on basic programing techniques that form the basis of bioinformatics. It is an excellent reference for a... read more

Table of Contents

  • Part I: Introduction to Unix/Linux
  • Part II: Programming in Python
  • Part III: Programming in R

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  • About the Book

     A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts:

    1. Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation.
    2. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs.
    3. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

    About the Contributors


    Shawn T. O'Neil, Oregon State University

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