From beginners to more advanced users of R, one of the leading programming languages used to analyze huge datasets generated by various types of genetic sequencing, “Computational Genomics with R” has something for everyone. Regardless of the reader’s scientific background, the book is designed to be accessible and practical.
“It was very important to us that readers could start doing their own data analysis as soon as possible,” says author Dr. Altuna Akalin, who heads the Bioinformatics and Omics Data Science Platform at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC). “We provide example code that allows you to get started right away and then expand.”
The book will be published by Chapman & Hall/CRC on December 29th. It starts with an overview of genomics, gene regulation and high-throughput sequencing, insight that will be more useful for programmers without a biology background. The rest of the book covers the basics of how to analyze several types of sequencing data using R, from plotting the data on a graph, to more complex analyses integrating multiple data types and unsupervised algorithms. Depending on the reader’s experience, they can work through the entire book, or jump to a particular type of analysis and follow the recipe.
An interdisciplinary field
Computational genomics is a highly interdisciplinary field, attracting scientists from physics and math, to biology and medicine. Through years of teaching, Akalin had identified gaps in knowledge among this audience, and had already written what amounted to a book for his students and lab members at MDC’s Berlin Institute of Medical Systems Biology (BIMSB); so he was well-positioned to write the textbook when approached by the publisher. Over the past two years, he has polished the manuscript, ensuring explanations are as clear as possible, and that all the code is up to date. Members of his lab, Vedran Franke, Bora Uyar and Jonathan Ronen, contributed three new chapters.
Akalin has been developing computational methods for analyzing and integrating large-scale genomics data for almost two decades. He notes that sequencing data is getting larger and more complex, making computational approaches increasingly unavoidable, but he finds too few scientists know how. “We need to train more scientists who can do this type of analysis,” Akalin says. “This text can help address that gap and train people up.”
Text: Laura Petersen