• Korean Journal of Clinical Laboratory Science
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• v.44 no.4
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• pp.167-177
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• 2012

Next-Generation Sequencing (NGS) is a term that means post-Sanger sequencing methods with high-throughput sequencing technologies. NGS parallelizes the sequencing process, producing thousands or millions of sequences at once. The latest NGS technologies use even single DNA molecule as a template and measures the DNA sequence directly via measuring electronic signals from the extension or degradation of DNA. NGS is making big impacts on biomedical research, molecular diagnosis and personalized medicine. The hospitals are rapidly adopting the use of NGS to help to patients understand treatment with sequencing data. As NGS equipments are getting smaller and affordable, many hospitals are in the process of setting up NGS platforms. In this review, the progress of NGS technology development and action mechanisms of representative NGS equipments of each generation were discussed. The key technological advances in the commercialized platforms were presented. As NGS platforms are a great concern in the healthcare area, the latest trend in the use of NGS and the prospect of NGS in the future in diagnosis and personalized medicine were also discussed.

• Journal of Life Science
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• v.24 no.11
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• pp.1258-1267
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• 2014

Developing economic traits with a high growth rate, robustness, and disease resistance in livestock is an important challenge. RFLP and AFLP are the classical methods used to develop economic traits. Whole-genome-based economic traits have recently been detected with the advent of next-generation sequencing (NGS) technologies. However, NGS technologies are rather costly for use in studies, and RNA-seq, RAD-Seq, RRL, MSG, and GBS have been used to overcome the issue of high costs. In this study, recent NGS-based studies were reviewed, particularly those that focused on minimum costs and maximum effects. Then, we presented further prospects on how to apply for selection of high economic-trait livestock.

• Journal of Life Science
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• v.25 no.3
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• pp.357-367
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• 2015

With the ongoing development of next-generation sequencing (NGS) platforms and advancements in the latest bioinformatics tools at an unprecedented pace, the ultimate goal of sequencing the human genome for less than $1,000 can be feasible in the near future. The rapid technological advances in NGS have brought about increasing demands for statistical methods and bioinformatics tools for the analysis and management of NGS data. Even in the early stages of the commercial availability of NGS platforms, a large number of applications or tools already existed for analyzing, interpreting, and visualizing NGS data. However, the availability of this plethora of NGS data presents a significant challenge for storage, analyses, and data management. Intrinsically, the analysis of NGS data includes the alignment of sequence reads to a reference, base-calling, and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection, and genome browsing. While the NGS technologies have allowed a massive increase in available raw sequence data, a number of new informatics challenges and difficulties must be addressed to improve the current state and fulfill the promise of genome research. This review aims to provide an overview of major NGS technologies and bioinformatics tools for NGS data analyses.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.107-112
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• 2013

With the progress of NGS technologies, large genome data have been exploded recently. To analyze such data effectively, the assistance of HPC technique is necessary. In this paper, we organized a genome analysis pipeline to call SNP from NGS data. To organize the pipeline efficiently under HPC environment, we analyzed the CPU utilization pattern of each pipeline steps. We found that sequence alignment is computing centric and suitable for parallelization. We also analyzed the performance of parallel open source alignment tools and found that alignment method utilizing many-core processor can improve the performance of genome analysis pipeline.

• ALGAE
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• v.31 no.2
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• pp.137-154
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• 2016

Next generation sequencing (NGS) technologies have revolutionized many areas of biological research due to the sharp reduction in costs that has led to the generation of massive amounts of sequence information. Analysis of large genome data sets is however still a challenging task because it often requires significant computer resources and knowledge of bioinformatics. Here, we provide a guide for an uninitiated who wish to analyze high-throughput NGS data. We focus specifically on the analysis of organelle genome and metagenome data and describe the current bioinformatic pipelines suited for this purpose.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.1
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• pp.1-6
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• 2021

Next-generation sequencing (NGS) technologies have changed the process of genetic diagnosis from a gene-by-gene approach to syndrome-based diagnostic gene panel sequencing (DPS), diagnostic exome sequencing (DES), and diagnostic genome sequencing (DGS). A priori information on the causative genes that might underlie a genetic condition is a prerequisite for genetic diagnosis before conducting clinical NGS tests. Theoretically, DPS, DES, and DGS do not require any information on specific candidate genes. Therefore, clinical NGS tests sometimes detect disease-related pathogenic variants in genes underlying different conditions from the initial diagnosis. These clinical NGS tests are expensive, but they can be a cost-effective approach for the rapid diagnosis of rare disorders with genetic heterogeneity, such as the glycogen storage disease, familial intrahepatic cholestasis, lysosomal storage disease, and primary immunodeficiency. In addition, DES or DGS may find novel genes that that were previously not linked to human diseases.

• Journal of Life Science
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• v.26 no.7
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• pp.855-867
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• 2016

The horse is relatively earlier domesticated animal species. Domesticated horses have been selected for their ability of racing, robustness, and disease-resistance. As a result, the thoroughbred horse genome has been condensed many genotypes related to exercise ability. In recent years, with the advent of NGS technologies, many studies were concentrated on finding superior genetic species in the horse genome in terms of genomics. Consequently, GWAS (Genome-wide Association study) is applied to horse genome, then genetic marker is revealed for superior racing ability. In addition, RNA-Seq is utilized as a method for analyze of whole transcript profiling in specific samples. By using this approach, specific gene expression patterns and transcript sequences can be revealed in various samples such as each individual, before and after exercise state, and each tissue. DNA methylation, a strong factor that regulate gene expression without the change of DNA sequence, have got a lot of attention. In horse genome, exercise- or individual-specific DNA methylation patterns were detected, and could be useful to develop selective marker of superior horses. MicroRNAs inhibit gene expression, and transposable elements accounted for half of the mammalian genome. These two elements are the crucial factors in functional genomics, and could be applied to the selection of superior horses. As the functional genomics and epigenomics advance, then these technologies introduced in this paper were applied to select superior horses. In this paper, the studies for selection of superior horses through genetic technologies, and development possibilities of these studies were discussed.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.23 no.2
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• pp.1-7
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• 2023

Inherited metabolic disorders (IMD) are a group of disorders involving various metabolic pathways. Genetic diagnosis of IMD has been challenging because of extremely heterogeneous nature and extensive laboratory and/or phenotype overlap. Conventional genetic diagnosis was a gene-by-gene approach that needs a priori information on the causative genes that might underlie the IMD. Recent implementation of next-generation sequencing (NGS) technologies has changed the process of genetic diagnosis from a gene-by-gene approach to simultaneous analysis of targeted genes possibly associated with the IMD using gene panels or using whole exome/genome sequencing (WES/WGS) covering entire human genes. Clinical NGS tests can be a cost-effective approach for the rapid diagnosis of IMD with genetic heterogeneity and are becoming standard diagnostic procedures.

• Genomics & Informatics
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• v.12 no.1
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• pp.2-11
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• 2014

After the initial enthusiasm of the human genome project, it became clear that without additional data pertaining to the epigenome, i.e., how the genome is marked at specific developmental periods, in different tissues, as well as across individuals and species-the promise of the genome sequencing project in understanding biology cannot be fulfilled. This realization prompted several large-scale efforts to map the epigenome, most notably the Encyclopedia of DNA Elements (ENCODE) project. While there is essentially a single genome in an individual, there are hundreds of epigenomes, corresponding to various types of epigenomic marks at different developmental times and in multiple tissue types. Unprecedented advances in next-generation sequencing (NGS) technologies, by virtue of low cost and high speeds that continue to improve at a rate beyond what is anticipated by Moore's law for computer hardware technologies, have revolutionized molecular biology and genetics research, and have in turn prompted innovative ways to reduce the problem of measuring cellular events involving DNA or RNA into a sequencing problem. In this article, we provide a brief overview of the epigenome, the various types of epigenomic data afforded by NGS, and some of the novel discoveries yielded by the epigenomics projects. We also provide ample references for the reader to get in-depth information on these topics.

• Genomics & Informatics
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• v.18 no.1
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• pp.10.1-10.9
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• 2020

Advancements in next generation sequencing (NGS) technologies have significantly increased the translational use of genomics data in the medical field as well as the demand for computational infrastructure capable processing that data. To enhance the current understanding of software and hardware used to compute large scale human genomic datasets (NGS), the performance and accuracy of optimized versions of GATK algorithms, including Parabricks and Sentieon, were compared to the results of the original application (GATK V4.1.0, Intel x86 CPUs). Parabricks was able to process a 50× whole-genome sequencing library in under 3 h and Sentieon finished in under 8 h, whereas GATK v4.1.0 needed nearly 24 h. These results were achieved while maintaining greater than 99% accuracy and precision compared to stock GATK. Sentieon's somatic pipeline achieved similar results greater than 99%. Additionally, the IBM POWER9 CPU performed well on bioinformatic workloads when tested with 10 different tools for alignment/mapping.