R Bioinformatics Cookbook

Over 60 recipes to model and handle real-life biological data using modern libraries from the R ecosystemKey FeaturesApply modern R packages to handle biological data using real-world examplesRepresent biological data with advanced visualizations suitable for research and publicationsHandle real-world problems in bioinformatics such as next-generation sequencing metagenomics and automating analysesBook DescriptionHandling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook you’ll explore all this and more tackling common and not-so-common challenges in the bioinformatics domain using real-world examples.This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor ggplot and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq phylogenetics genomics and sequence analysis. As you progress you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.What you will learnEmploy Bioconductor to determine differential expressions in RNAseq dataRun SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and IndelsUse ggplot to create and annotate a range of visualizationsQuery external databases with Ensembl to find functional genomics informationExecute large-scale multiple sequence alignment with DECIPHER to perform comparative genomicsUse d3.js and Plotly to create dynamic and interactive web graphicsUse k-nearest neighbors support vector machines and random forests to find groups and classify dataWho this book is forThis book is for bioinformaticians data analysts researchers and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites. About the Author Professor Dan MacLean has a Ph.D. in molecular biology from the University of Cambridge and gained postdoctoral experience in genomics and bioinformatics at Stanford University in California. Dan is now a Honorary Professor in the School of Computing Sciences at the University of East Anglia. He has worked in bioinformatics and plant pathogenomics specializing in R and Bioconductor and developing analytical workflows in bioinformatics genomics genetics image analysis and proteomics at The Sainsbury Laboratory since 2006. Dan has developed and published software packages in R Ruby and Python with over 100000 downloads combined.