Data Science for Genomics

Data Science for Genomics presents the foundational concepts of Data Science as they pertain to Genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. The authors begin by presenting an introduction to Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes, and Proteomes as basic concepts of molecular biology, along with DNA and the key features of the human genome, as well as the genomes of eukaryotes and prokaryotes in general. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR, that were used in the pre-genomics era to examine individual genes. Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell are presented, as well as the important issue of how chromatin structure influences genome expression. Readers will learn about the assembly of the transcription initiation complexes of prokaryotes and eukaryotes, along with a detailed discussion of DNA-binding proteins, these playing the central roles in the initial stages of genome expression. Each aspect of Genomics is aligned with associated Data Science concepts and methods including Machine Learning, Deep Learning, Artificial Intelligence, Data Privacy and Data Trust, Visual Data Analysis and Complex Data Analysis, Big Data Programming with Apache Spark and Hadoop, Blockchain technology for securing Genomic data, Cloud, Edge and Fog computing, as well as future research directions. Provides a detailed explanation of Data Science concepts, methods, and algorithms, reinforced by practical examples applied to Genomics Presents a road map of future trends suitable for innovative Data Science research and practice Includes topics such as Blockchain technology for securing data at end user/server side Presents real world case studies, open issues, and challenges faced in Genomics, including future research directions and a separate chapter for Ethical Concerns