Book Image

R Bioinformatics Cookbook - Second Edition

By : Dan MacLean
Book Image

R Bioinformatics Cookbook - Second Edition

By: Dan MacLean

Overview of this book

The updated second edition of R Bioinformatics Cookbook takes a recipe-based approach to show you how to conduct practical research and analysis in computational biology with R. You’ll learn how to create a useful and modular R working environment, along with loading, cleaning, and analyzing data using the most up-to-date Bioconductor, ggplot2, and tidyverse tools. This book will walk you through the Bioconductor tools necessary for you to understand and carry out protocols in RNA-seq and ChIP-seq, phylogenetics, genomics, gene search, gene annotation, statistical analysis, and sequence analysis. As you advance, you'll find out how to use Quarto to create data-rich reports, presentations, and websites, as well as get a clear understanding of how machine learning techniques can be applied in the bioinformatics domain. The concluding chapters will help you develop proficiency in key skills, such as gene annotation analysis and functional programming in purrr and base R. Finally, you'll discover how to use the latest AI tools, including ChatGPT, to generate, edit, and understand R code and draft workflows for complex analyses. By the end of this book, you'll have gained a solid understanding of the skills and techniques needed to become a bioinformatics specialist and efficiently work with large and complex bioinformatics datasets.
Table of Contents (16 chapters)

Debugging and improving code with ChatGPT

ChatGPT is capable of debugging code due to its extensive training on programming-related text. It has acquired knowledge of common programming patterns and errors, allowing it to understand code snippets, identify potential issues, and provide suggestions or improvements. It can identify minor errors by highlighting syntax issues such as missing brackets, incorrect function calls, and invalid variable assignments. ChatGPT helps troubleshooting by asking clarifying questions to better understand the problem and guide developers toward the root cause, and can aid in code optimization by suggesting efficiency improvements, identifying performance bottlenecks, and proposing more optimized implementations. Lastly, the debugging process with ChatGPT can foster learning and exploration, enabling developers to discover new concepts, programming techniques, or functionalities of specific libraries or packages. In this recipe, we’ll walk through...