**1. Introduction**

Next-generation sequencing (NGS) refers to the deep, high-throughput, in-parallel DNA sequencing technologies developed a few decades after the Sanger DNA sequencing method first emerged in 1977 and then dominated for three decades [1, 2]. The NGS technologies are different from the Sanger method in that they provide massively parallel analysis, extremely high-throughput from multiple samples at much reduced cost [3]. Millions to billions of DNA

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nucleotides can be sequenced in parallel, yielding substantially more throughput and mini‐ mizing the need for the fragment-cloning methods that were used with Sanger sequencing [4]. The second-generation sequencing methods are characterized by the need to prepare amplified sequencing libraries before undertaking sequencing of the amplified DNA clones, whereas third-generation single molecular sequencing can be done without the need for creating the time-consuming and costly amplification libraries [5]. The parallelization of a high number of sequencing reactions by NGS was achieved by the miniaturization of sequencing reactions and, in some cases, the development of microfluidics and improved detection systems [6]. The time needed to generate the gigabase (Gb)-sized sequences by NGS was reduced from many years to only a few days or hours, with an accompanying massive price reduction. For example, as part of the Human Genome Project, the J. C. Venter genome [7] took almost 15 years to sequence at a cost of more than 1 million dollars using the Sanger method, whereas the J. D. Watson (1962 Nobel Prize winner) genome was sequenced by NGS using the 454 Genome Sequencer FLX with about the same 7.5x coverage within 2 months and for approximately 100th of the price [8]. The cost of sequencing the bacterial genome is now possible at about \$1000 (https://www.nanoporetech.com), and the large-scale whole-genome sequencing (WGS) of 2,636 Icelanders [9] has brought some of the aims of the 1000 Genomes Project [10] to abrupt fruition.

Rapid progress in NGS technology and the simultaneous development of bioinformatics tools has allowed both small and large research groups to generate *de novo* draft genome sequences for any organism of interest. Apart from using NGS for WGS [11], these technologies can be used for whole transcriptome shotgun sequencing (WTSS) — also called RNA sequencing (RNA-seq) [12], whole-exome sequencing (WES) [13], targeted (TS) or candidate gene se‐ quencing (CGS) [14–16], and methylation sequencing (MeS) [17]. RNA-seq can be used to identify all transcriptional activities (coding and noncoding) or a select subset of targeted RNA transcripts within a given sample [12], and it provides a more precise and sensitive measure‐ ment of gene expression levels than microarrays in the analysis of many samples [18–21]. In contrast to WGS, WES provides coverage for more than 95% of human exons to investigate the protein-coding regions (CDS) of the genome and identify coding variants or SNPs when WGS and WTSS are not practical or necessary [13]. Since the exome represents less than 2% of the human genome, it is the cost-effective alternative to WGS and RNA-seq in the study of human genetics and disease [13]. However, WGS may be preferred over WES because it provides more data with better uniformity of read coverage on disease-associated variants and reveals polymorphisms outside coding regions and genomic rearrangements [19, 22]. The analysis of the methylome by MeS complements WGS, WES, and CGS to determine the active methylation sites and the epigenetic markers that regulate gene expression, epistructural base variations, imprinting, development, differentiation, disease, and the epigenetic state [23–30]. The impact of NGS technology is indeed egalitarian in that it allows both small and large research groups the possibility to provide answers and solutions to many different problems and questions in the fields of genetics and biology, including those in medicine, agriculture, forensic science, virology, microbiology, and marine and plant biology.

The aim of this chapter is to provide an overview of the advances, applications, and challenges of NGS, starting with a history of first-generation sequencing followed by the major NGS platforms, the bioinformatics issues confronting NGS data storage and analysis, and the applications and challenges for biology and medicine in the world of "omic" expansion.
