Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing

Sang Tae Park, Jayoung Kim, Sang Tae Park, Jayoung Kim

Abstract

This article is a mini-review that provides a general overview for next-generation sequencing (NGS) and introduces one of the most popular NGS applications, whole genome sequencing (WGS), developed from the expansion of human genomics. NGS technology has brought massively high throughput sequencing data to bear on research questions, enabling a new era of genomic research. Development of bioinformatic software for NGS has provided more opportunities for researchers to use various applications in genomic fields. De novo genome assembly and large scale DNA resequencing to understand genomic variations are popular genomic research tools for processing a tremendous amount of data at low cost. Studies on transcriptomes are now available, from previous-hybridization based microarray methods. Epigenetic studies are also available with NGS applications such as whole genome methylation sequencing and chromatin immunoprecipitation followed by sequencing. Human genetics has faced a new paradigm of research and medical genomics by sequencing technologies since the Human Genome Project. The trend of NGS technologies in human genomics has brought a new era of WGS by enabling the building of human genomes databases and providing appropriate human reference genomes, which is a necessary component of personalized medicine and precision medicine.

Keywords: Epigenomics; Genomics; High-Throughput Nucleotide Sequencing; Human Genome Project; Sequence Analysis, RNA.

Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.
Comparison of Sanger sequencing and next-generation sequencing (NGS). (A) Shotgun Sanger sequencing workflow. (B) Shotgun based NGS workflow with cyclic-array method. This figure shows the basic process of the 2 technologies and also shows 3 major improvements in NGS from Sanger sequencing described in that review. Adapted from Shendure J, et al. Nat Biotechnol 2008;26:1135-45 [4].
Fig. 2.
Fig. 2.
Graph of “Cost per Genome.” This graph illustrates the nature of the reductions in sequencing costs and also hypothetical data reflecting Moore’s Law. Adapted from National Human Genome Research Institute (NHGRI) [9].
Fig. 3.
Fig. 3.
(A) Geographic map of 5 sequenced genomes. MT type represents the mitochondrial haplogroup. Illumina GA, ABI 3730, and GS FLX represent the sequencing platform used for these sequencing projects. (B) Diagram of single nucleotide polymorphism overlapping results between 5 genomes. Adapted from Kim JI, et al. Nature 2009;460:1011-5 [28].
Fig. 4.
Fig. 4.
Whole genome sequencing application workflow based on HiSeq X system. This figure is provided from a genomic sequencing service provider with HiSeq X system. Actual workflow will vary by institutions and companies. BCL, basecall file; BAM, the binary version of a SAM (sequence alignment/map); mark-dup, marks duplicate reads; SNP, single nucleotide polymorphism; INDEL, insertion or deletion of bases; SV, structural variation; VCF, variant call format; CNV, copy number variation.

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