This website provides information on setup of the tools introduced in the book, access to the code as well as exercises and solutions. Furthermore, you will find additional material, updates and errata. Please contact us if you find issues in the book or would like to suggest new topics for the book or website.
We would also love to hear from readers and instructors who use the book in their classroom.
Computing Skills for Biologists – A Toolbox is ideal for students and scientists who want to improve their computational skills, as well as instructors who want to present the computing tools that are essential for biological research in the twenty-first century. Both novice and experienced scientists will increase their efficiency, by learning how to build automated and reproducible pipelines for biological data analysis, visualization and publication.
We introduce a set of languages and tools to enable you to pick the appropriate tool for each task at hand. Specifically, you will learn how to use bash scripting, Python, Regular Expressions, R, LaTeX and SQL. The book presents fundamental computational concepts such as finding and using available software packages, writing your own functions, and constructing well-documented pipelines. By introducing version control and debugging, we furthermore emphasize reproducibility and organization of your work so it is easy to repeat, trouble-shoot and share.
Stressing practice rather than theory, the book’s examples and exercises are drawn from published biological data, and ask the reader to solve cogent problems spanning a wide breadth of biological disciplines, including ecology, genetics, microbiology, and molecular biology. The book uses free software and code that can be run on any platform.
We are fascinated by computer generated (arithmetic) art. Using repetitions of simple processes the artist creates an image that is paradoxically both chaotic and structured.
Our cover is graced by art of Desmond Paul Henry. He was one of the first British artists to experiment with machine-generated visual effects at the time of the emerging global computer art movement of the 1960s. You can see more on DP Henry's intriguing art on desmondhenry.com.
Our book is based on Stefano's course “Introduction to Scientific Computing" at the University of Chicago, but we started writing the book from scratch in the beginning of 2015. We submitted the final manuscript to Princeton University Press in August 2018.
How come there are so few figures in Computing Skills for Biologists – A Toolbox? Moreover, why are there exactly zero in the chapter on visualizing data? The irony is not lost on us.
The reason is that we wrote a book for graduate students and we wanted to price it for graduate students. Including just a single color image in the book would have made the book a lot more expansive.
We could have included black and white graphics in the visualization chapter. However, that would have severely limited our ability to introduce some topics and features that rely on plotting with colors. Printing these in black and white might have been of little use.
Instead, we reasoned that learners see the resulting graphics on their computer when they type the code in the book. Furthermore, learners can compare their results with a pdf that shows all code and graphics. This pdf comes with the book's repository and is available for download here.
Stefano loves science — and good espresso. His laboratory at the University of Chicago is focused on the theory of ecology, studying food webs, and the stability of ecological systems. He's also interested in the budding field of “Science of Science", for example analyzing big data to learn how should we write papers, and even measuring the prevalence of nepotism in Italian academic hires — which made him a persona non grata in certain circles.
Computational Skills for Biologists – A Toolbox morphed out of the course “Introduction to Scientific Computing for Biologists" which he created to bring the members of his lab up to speed with the computational skills required to produce accurate, reproducible (and fun!) science.
Madlen enjoys learning new things and teaching others what she learned. That's why she wanted Stefano to write Computational Skills for Biologists – A Toolbox with her — so others would have an easier time than herself to learn how to code and be efficient scientists.
These days, she employs her computational toolbox at a large bank to turn data into knowledge, teach good coding practices, and convert SAS users into R users.