Computational Genomics with R serves as an introductory resource for those new to genomic data analysis, while also providing advanced practitioners with guidance on sophisticated data analysis techniques in genomics. The book covers a wide range of topics, including R programming, machine learning, statistics, and the latest genomic data analysis techniques. The text offers accessible information and explanations, always keeping the genomics context in mind. Additionally, the book includes practical and well-documented examples in R, allowing readers to easily analyze their own data by reusing the provided code. Given the interdisciplinary nature of computational genomics, individuals with different backgrounds may have different starting points. For instance, a biologist may choose to skip sections on basic genome biology and focus on R programming, while a computer scientist may prefer to begin with genome biology.
After reading this book, you will have a solid foundation in R and be able to apply it to specialized computational genomics tasks, such as utilizing Bioconductor packages. You will also gain familiarity with important statistical concepts, as well as supervised and unsupervised learning techniques that are crucial for data modeling and exploratory analysis of high-dimensional data. Additionally, you will develop an understanding of genomic intervals and the operations performed on them, which are essential for tasks like aligned read counting and genomic feature annotation. Furthermore, you will acquire the necessary skills for processing and quality checking high-throughput sequencing data. You will also be able to conduct sequence analysis, such as calculating GC content for specific genome regions or identifying transcription factor binding sites. Moreover, you will become acquainted with various visualization techniques commonly used in genomics, including heatmaps, meta-gene plots, and genomic track visualization. Lastly, you will gain knowledge in analyzing different types of high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. Additionally, you will learn basic techniques for integrating and interpreting multi-omics datasets.
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