Description
Book SynopsisComputational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology.
After reading:
- You will have the basics of R and be able to dive right into specialized uses of R
Trade Review
'This book provides a basic overview of computational tools developed in R for carrying out data analyses in genomics. It can be a valuable companion for anyone whowants to utilise the computational tools developed within the Bioconductor and R environments for education and research. This book’s main target audience are students of computational biology to get a first look at the diversity of machine learning methods. Thebook will also servewell biomedical researchers needing a guide to packages that can help them with the analysis of data that they encounter in their work.'
- Krzysztof Podgórski, International Statistical Review (2021) doi: 10.1111/insr.12453
Table of Contents
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- Introduction to Genomics. 2. Introduction to R for Genomic Data Analysis 3. Statistics for Genomics 4. Exploratory Data Analysis with Unsupervised Machine Learning 5. Predictive Modeling with Supervised Machine Learning 6. Operations on Genomic Intervals and Genome Arithmetic 7. Quality Check, Processing and Alignment of High-throughput Sequencing Reads 8. RNA-seq Analysis 9. ChIP-seq analysis 10. DNA methylation analysis using bisulfite sequencing 11. Multi-omics Analysis.
