Sometimes, we'll have a list of protein sequences that have come from some analysis or experiment that are in some way biologically related—for example, they may all bind the same target—and we will want to determine the parts of those proteins that are responsible for the action. Domain and motif finding, as we've done in the preceding recipes, can be helpful, but only if we've seen the domains before or the sequence is particularly well conserved or statistically over-represented. A different approach is to try machine learning in which we build a model that can classify our proteins of interest accurately and then use the properties of the model to show us which parts of the proteins result in the classification. We'll take that approach in this recipe; specifically, we'll train...
R Bioinformatics Cookbook
By :
R Bioinformatics Cookbook
By:
Overview of this book
Handling 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.
Related Content you might be interested in
Current Title:
R Bioinformatics Cookbook
Table of Contents (13 chapters)
Preface
Free Chapter
Performing Quantitative RNAseq
Finding Genetic Variants with HTS Data
Searching Genes and Proteins for Domains and Motifs
Phylogenetic Analysis and Visualization
Metagenomics
Proteomics from Spectrum to Annotation
Producing Publication and Web-Ready Visualizations
Working with Databases and Remote Data Sources
Useful Statistical and Machine Learning Methods
Programming with Tidyverse and Bioconductor
Building Objects and Packages for Code Reuse
Other Books You May Enjoy
Customer Reviews