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. 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. 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, and it provides a more precise and sensitive measurement of gene expression levels than microarrays in the analysis of many samples. Apart from using NGS for WGS, these technologies can be used for whole transcriptome shotgun sequencing (WTSS) - also called RNA sequencing (RNA-seq), whole-exome sequencing (WES), targeted (TS) or candidate gene sequencing (CGS), and methylation sequencing (MeS). Rapid progress in NGS technology and the simultaneous development of bioinformatics tools has allowed both small and large research groups to generate de novodraft genome sequences for any organism of interest. The cost of sequencing the bacterial genome is now possible at about $1000 (), and the large-scale whole-genome sequencing (WGS) of 2,636 Icelanders has brought some of the aims of the 1000 Genomes Project to abrupt fruition. 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. Venter genome took almost 15 years to sequence at a cost of more than 1 million dollars using the Sanger method, whereas the J. For example, as part of the Human Genome Project, the J. 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. 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. 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. Millions to billions of DNA nucleotides can be sequenced in parallel, yielding substantially more throughput and minimizing the need for the fragment-cloning methods that were used with Sanger sequencing. 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. 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. In this chapter, the advances, applications, and challenges of NGS are reviewed 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 impacts made in the fields of genetics, biology, agriculture, and medicine in the brave, new world of ”omics.” NGS today is more than ever about how different organisms use genetic information and molecular biology to survive and reproduce with and without mutations, disease, and diversity within their population networks and changing environments.
The vast amounts of data generated by NGS have broadened our understanding of structural and functional genomics through the concepts of “omics” ranging from basic genomics to integrated systeomics, providing new insight into the workings and meaning of genetic conservation and diversity of living things. NGS is the choice for large-scale genomic and transcriptomic sequencing because of the high-throughput production and outputs of sequencing data in the gigabase range per instrument run and the lower cost compared to the traditional Sanger first-generation sequencing method. Next-generation sequencing (NGS) technologies using DNA, RNA, or methylation sequencing have impacted enormously on the life sciences.
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