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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Genomics & Informatics
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Journal DOI :
Korea Genome Organization
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Volume & Issues
Volume 10, Issue 4 - Dec 2012
Volume 10, Issue 3 - Sep 2012
Volume 10, Issue 2 - Jun 2012
Volume 10, Issue 1 - Mar 2012
Selecting the target year
Application of Whole Exome Sequencing to Identify Disease-Causing Variants in Inherited Human Diseases
Goh, Gerald ; Choi, Murim ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 214~219
DOI : 10.5808/GI.2012.10.4.214
The recent advent of next-generation sequencing technologies has dramatically changed the nature of biomedical research. Human genetics is no exception－it has never been easier to interrogate human patient genomes at the nucleotide level to identify disease-associated variants. To further facilitate the efficiency of this approach, whole exome sequencing (WES) was first developed in 2009. Over the past three years, multiple groups have demonstrated the power of WES through robust disease-associated variant discoveries across a diverse spectrum of human diseases. Here, we review the application of WES to different types of inherited human diseases and discuss analytical challenges and possible solutions, with the aim of providing a practical guide for the effective use of this technology.
A Short History of the Genome-Wide Association Study: Where We Were and Where We Are Going
Ikegawa, Shiro ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 220~225
DOI : 10.5808/GI.2012.10.4.220
Recent rapid advances in genetic research are ushering us into the genome sequence era, where an individual's genome information is utilized for clinical practice. The most spectacular results of the human genome study have been provided by genome-wide association studies (GWASs). This is a review of the history of GWASs as related to my work. Further efforts are necessary to make full use of its potential power to medicine.
Transposable Elements: No More 'Junk DNA'
Kim, Yun-Ji ; Lee, Jungnam ; Han, Kyudong ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 226~233
DOI : 10.5808/GI.2012.10.4.226
Since the advent of whole-genome sequencing, transposable elements (TEs), just thought to be 'junk' DNA, have been noticed because of their numerous copies in various eukaryotic genomes. Many studies about TEs have been conducted to discover their functions in their host genomes. Based on the results of those studies, it has been generally accepted that they have a function to cause genomic and genetic variations. However, their infinite functions are not fully elucidated. Through various mechanisms, including de novo TE insertions, TE insertion-mediated deletions, and recombination events, they manipulate their host genomes. In this review, we focus on Alu, L1, human endogenous retrovirus, and short interspersed element/variable number of tandem repeats/Alu (SVA) elements and discuss how they have affected primate genomes, especially the human and chimpanzee genomes, since their divergence.
Understanding Disease Susceptibility through Population Genomics
Han, Seonggyun ; Lee, Junnam ; Kim, Sangsoo ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 234~238
DOI : 10.5808/GI.2012.10.4.234
Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.
Finding Genetic Risk Factors of Gestational Diabetes
Kwak, Soo Heon ; Jang, Hak C. ; Park, Kyong Soo ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 239~243
DOI : 10.5808/GI.2012.10.4.239
Gestational diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care.
Association Analysis of Reactive Oxygen Species-Hypertension Genes Discovered by Literature Mining
Lim, Ji Eun ; Hong, Kyung-Won ; Jin, Hyun-Seok ; Oh, Bermseok ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 244~248
DOI : 10.5808/GI.2012.10.4.244
Oxidative stress, which results in an excessive product of reactive oxygen species (ROS), is one of the fundamental mechanisms of the development of hypertension. In the vascular system, ROS have physical and pathophysiological roles in vascular remodeling and endothelial dysfunction. In this study, ROS-hypertension-related genes were collected by the biological literature-mining tools, such as SciMiner and gene2pubmed, in order to identify the genes that would cause hypertension through ROS. Further, single nucleotide polymorphisms (SNPs) located within these gene regions were examined statistically for their association with hypertension in 6,419 Korean individuals, and pathway enrichment analysis using the associated genes was performed. The 2,945 SNPs of 237 ROS-hypertension genes were analyzed, and 68 genes were significantly associated with hypertension (p < 0.05). The most significant SNP was rs2889611 within MAPK8 (p =
; odds ratio, 0.82; confidence interval, 0.75 to 0.90). This study demonstrates that a text mining approach combined with association analysis may be useful to identify the candidate genes that cause hypertension through ROS or oxidative stress.
A Fosmid Cloning Strategy for Detecting the Widest Possible Spectrum of Microbes from the International Space Station Drinking Water System
Choi, Sangdun ; Chang, Mi Sook ; Stuecker, Tara ; Chung, Christine ; Newcombe, David A. ; Venkateswaran, Kasthuri ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 249~255
DOI : 10.5808/GI.2012.10.4.249
In this study, fosmid cloning strategies were used to assess the microbial populations in water from the International Space Station (ISS) drinking water system (henceforth referred to as Prebiocide and Tank A water samples). The goals of this study were: to compare the sensitivity of the fosmid cloning strategy with that of traditional culture-based and 16S rRNA-based approaches and to detect the widest possible spectrum of microbial populations during the water purification process. Initially, microbes could not be cultivated, and conventional PCR failed to amplify 16S rDNA fragments from these low biomass samples. Therefore, randomly primed rolling-circle amplification was used to amplify any DNA that might be present in the samples, followed by size selection by using pulsed-field gel electrophoresis. The amplified high-molecular- weight DNA from both samples was cloned into fosmid vectors. Several hundred clones were randomly selected for sequencing, followed by Blastn/Blastx searches. Sequences encoding specific genes from Burkholderia, a species abundant in the soil and groundwater, were found in both samples. Bradyrhizobium and Mesorhizobium, which belong to rhizobia, a large community of nitrogen fixers often found in association with plant roots, were present in the Prebiocide samples. Ralstonia, which is prevalent in soils with a high heavy metal content, was detected in the Tank A samples. The detection of many unidentified sequences suggests the presence of potentially novel microbial fingerprints. The bacterial diversity detected in this pilot study using a fosmid vector approach was higher than that detected by conventional 16S rRNA gene sequencing.
Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data
Lee, Sungyoung ; Kwon, Min-Seok ; Park, Taesung ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 256~262
DOI : 10.5808/GI.2012.10.4.256
Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (
) interactions. However, the biological interpretation of
interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified
interactions. The proposed network graph analysis consists of three steps. The first step is for performing
interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified
interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform
interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified
interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of
QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data
Kim, Nayoung ; Park, Herin ; He, Ningning ; Lee, Hyeon Young ; Yoon, Sukjoon ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 263~265
DOI : 10.5808/GI.2012.10.4.263
We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.
Developing JSequitur to Study the Hierarchical Structure of Biological Sequences in a Grammatical Inference Framework of String Compression Algorithms
Galbadrakh, Bulgan ; Lee, Kyung-Eun ; Park, Hyun-Seok ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 266~270
DOI : 10.5808/GI.2012.10.4.266
Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.
Preliminary Study of Bioinformatics Patents and Their Classifications Registered in the KIPRIS Database
Park, Hyun-Seok ;
Genomics & Informatics, volume 10, issue 4, 2012, Pages 271~274
DOI : 10.5808/GI.2012.10.4.271
Whereas a vast amount of new information on bioinformatics is made available to the public through patents, only a small set of patents are cited in academic papers. A detailed analysis of registered bioinformatics patents, using the existing patent search system, can provide valuable information links between science and technology. However, it is extremely difficult to select keywords to capture bioinformatics patents, reflecting the convergence of several underlying technologies. No single word or even several words are sufficient to identify such patents. The analysis of patent subclasses can provide valuable information. In this paper, I did a preliminary study of the current status of bioinformatics patents and their International Patent Classification (IPC) groups registered in the Korea Intellectual Property Rights Information Service (KIPRIS) database.