R Bioinformatics Cookbook: Utilize R packages for bioinformatics, genomics, data science, and machine learning , Second Edition

MacLean

₹800 per month

4.7 (3 Ratings)

Paperback Oct 2023 396 pages 2nd Edition

MacLean

₹800 per month

4.7 (3 Ratings)

Paperback Oct 2023 396 pages 2nd Edition

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Key benefits

Apply modern R packages to process biological data using real-world examples
Represent biological data with advanced visualizations and workflows suitable for research and publications
Solve real-world bioinformatics problems such as transcriptomics, genomics, and phylogenetics
Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning via a recipe-based approach. Working knowledge of the R programming language and basic knowledge of bioinformatics are prerequisites.

What you will learn

Set up a working environment for bioinformatics analysis with R
Import, clean, and organize bioinformatics data using tidyr
Create publication-quality plots, reports, and presentations using ggplot2 and Quarto
Analyze RNA-seq, ChIP-seq, genomics, and next-generation genetics with Bioconductor
Search for genes and proteins by performing phylogenetics and gene annotation
Apply ML techniques to bioinformatics data using mlr3
Streamline programmatic work using iterators and functional tools in the base R and purrr packages
Use ChatGPT to create, annotate, and debug code and workflows

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Customer reviews

Rating distribution

4.7

(3 Ratings)

5 star 66.7%

4 star 33.3%

3 star 0%

2 star 0%

1 star 0%

Rudrendu Kumar Paul Nov 04, 2023

The R Bioinformatics Cookbook by Dan MacLean is a practical, comprehensive guide aimed at bioinformaticians looking to expand their data analysis skills using R and Bioconductors. With the author's extensive background in genomics, molecular biology, and bioinformatics, readers can trust that the book provides authoritative content.A major strength of the cookbook format is it supplies readers with recipe-based solutions to tackle common bioinformatics challenges. Rather than just theory, professionals get hands-on guidance to put concepts into practice. The book spans a versatile range of topics - from the initial setup of the R environment to sophisticated methods like machine learning. This ensures readers can find both introductory building blocks as well as more advanced techniques to round out their skills.Notably, the book thoroughly covers specialized packages and frameworks within the R ecosystem that are tailored for bioinformatics. Readers are immersed in the diverse capabilities of Bioconductor along with packages like ggplot2 for publication-quality graphics and mlr3 for machine learning. The focus on R capabilities designed for biological data analysis is a distinguishing factor.The content covers crucial bioinformatics techniques such as RNA-seq analysis, variant calling from DNA sequencing data, statistical modeling, and phylogenetic approaches. There is also a significant emphasis on data visualization and interpretation - essential skills for research. Interactive elements utilizing Shiny and Quarto demonstrate new avenues for impactful data presentation.While the book is geared towards those with some existing R familiarity, beginners may need help due to the assumed knowledge and density of information spanning many techniques. However, intermediate to advanced R users in bioinformatics will find an invaluable guide for expanding their toolkit in a hands-on manner. They can level up skills in both fundamental and cutting-edge data analysis areas.Additional topics could potentially enhance the book even further. As biological datasets explode in size, guidance on leveraging cloud computing could prove useful. More coverage of sophisticated machine learning methods like deep learning would keep readers abreast of the latest techniques for modeling complex data. Single-cell sequencing and metagenomics represent rapidly growing areas where extra content would make this guide more comprehensive for the field.The R Bioinformatics Cookbook is a practical, authoritative resource for bioinformaticians looking to hone their R skills for impactful data analysis. The hands-on solutions, specialized coverage of R packages, and techniques like statistical modeling and visualization make it a robust guide for intermediate to advanced practitioners seeking to expand their toolkits. While beginners may need more foundational content, the book's recipe-based approach helps professionals readily apply concepts to real-world biological studies.

Amazon Verified review

Amazon Customer Nov 04, 2023

AI practitioners looking to upgrade their data analysis skills with R, the R Bioinformatics Cookbook by Dan MacLean offers an authoritative, practical guide. True to its name, the cookbook format provides proven recipes to solve common challenges faced in biological research. Rather than just theory, professionals get hands-on solutions readily applicable to real-world work.The book strikes an effective balance between breadth and depth across two dimensions. First, it spans a diverse range of topics - from R environment setup to advanced machine learning. Second, it delves deeply into specialized R packages tailored for bioinformatics within the expansive Bioconductor ecosystem. Readers tour the diverse landscape of tools designed specifically for genomics and computational biology.Notably, the book moves beyond just core techniques to incorporate cutting-edge methods poised to shape the future. The coverage of sophisticated machine learning using mlr3 and interactive dashboards with Shiny/Quarto keeps readers ahead of the curve. While leveraging time-tested tools like ggplot2 for publication-quality graphics, the book also showcases next-gen avenues for analysis and communication.The content is firmly rooted in the practical needs of bioinformaticians. Crucial workflows like sequencing data analysis, statistical modeling, and phylogenetics all receive thorough treatment. Each chapter focuses on a single technique, ensuring digestible depth. And code is structured to promote understanding and extensibility rather than just solutions.For readers with some existing R familiarity, the book serves as an invaluable springboard to enhanced productivity. It provides building blocks to level up both fundamental and advanced skills. However, complete beginners may need supplementary learning first to establish core competencies before benefiting fully. The assumed knowledge makes this more suited for intermediate learners onward.To make this comprehensive guide even more holistic, additional topics on large-scale cloud computing, deep learning for omics data, and single-cell analysis could prove valuable amendments. As data complexity escalates, guidance on these emerging fronts would increase the book's utility for tackling real-world biological complexities.The practitioners seeking to expand their R toolkit in order to derive greater insights from modern molecular datasets, the R Bioinformatics Cookbook hits the sweet spot between theory and practice. Its competent stewardship of both foundational and innovative R-based techniques makes it a recipe for success in bioinformatics.

Amazon Verified review

Om S Nov 09, 2023

In the second edition of the "R Bioinformatics Cookbook," readers are taken on a hands-on journey through over 80 recipes, providing a practical guide to conducting research and analysis in computational biology using the R ecosystem. The book emphasizes real-world applications, offering a treasure trove of modern R packages for processing biological data with tangible examples. From setting up a functional R working environment to analyzing RNA-seq, ChIP-seq, genomics, and more, each chapter introduces essential tools and techniques for bioinformatics enthusiasts.The book's strength lies in its recipe-based approach, allowing bioinformaticians, data analysts, researchers, and R developers to tackle intermediate-to-advanced biological problems effectively. Notably, the inclusion of Bioconductor, ggplot2, and Quarto tools enhances the reader's ability to represent biological data through advanced visualizations suitable for research and publication. The concluding chapters delve into machine learning with mlr3 and harnessing the power of ChatGPT for code generation and workflow understanding. With a clear structure and a focus on practical applications, this cookbook equips readers to become proficient bioinformatics specialists, navigating the complexities of large and intricate biological datasets.

Amazon Verified review

R Bioinformatics Cookbook: Utilize R packages for bioinformatics, genomics, data science, and machine learning , Second Edition

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