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R Bioinformatics Cookbook

R Bioinformatics Cookbook

By : MacLean, Dr Dan Maclean
2.7 (3)
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R Bioinformatics Cookbook

R Bioinformatics Cookbook

2.7 (3)
By: MacLean, Dr Dan Maclean

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.
Table of Contents (13 chapters)
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Aligning genomic length sequences with DECIPHER

Aligning sequences longer than genes and proteins, such as contigs from assembly projects, chromosomes, or whole genomes is a tricky task and one for which we need different techniques than those for short sequences. The longer sequences get, the harder they are to align. Long alignments are especially costly in terms of the computational time taken, since the algorithms that are effective on short sequences take up exponentially more time with increasing sequence length. Performing longer alignments generally starts with finding short anchor alignments and working the alignment out from there. We typically end up with blocks of synteny—regions that match well between the different genome alignments.

In this recipe, we'll look at the DECIPHER package for genome length alignments. We'll use some chloroplast genomes...

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