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Transposable Elements and Genome Size Variations in Plants
Lee, Sung-Il,Kim, Nam-Soo Korea Genome Organization 2014 Genomics & informatics Vol.12 No.3
Although the number of protein-coding genes is not highly variable between plant taxa, the DNA content in their genomes is highly variable, by as much as 2,056-fold from a 1C amount of 0.0648 pg to 132.5 pg. The mean 1C-value in plants is 2.4 pg, and genome size expansion/contraction is lineage-specific in plant taxonomy. Transposable element fractions in plant genomes are also variable, as low as ~3% in small genomes and as high as ~85% in large genomes, indicating that genome size is a linear function of transposable element content. Of the 2 classes of transposable elements, the dynamics of class 1 long terminal repeat (LTR) retrotransposons is a major contributor to the 1C value differences among plants. The activity of LTR retrotransposons is under the control of epigenetic suppressing mechanisms. Also, genome-purging mechanisms have been adopted to counter-balance the genome size amplification. With a wealth of information on whole-genome sequences in plant genomes, it was revealed that several genome-purging mechanisms have been employed, depending on plant taxa. Two genera, Lilium and Fritillaria, are known to have large genomes in angiosperms. There were twice times of concerted genome size evolutions in the family Liliaceae during the divergence of the current genera in Liliaceae. In addition to the LTR retrotransposons, non-LTR retrotransposons and satellite DNAs contributed to the huge genomes in the two genera by possible failure of genome counter-balancing mechanisms.
Whole-genome sequence analysis through online web interfaces: a review
Gunasekara, A.W.A.C.W.R.,Rajapaksha, L.G.T.G.,Tung, T.L. Korea Genome Organization 2022 Genomics & informatics Vol.20 No.1
The recent development of whole-genome sequencing technologies paved the way for understanding the genomes of microorganisms. Every whole-genome sequencing (WGS) project requires a considerable cost and a massive effort to address the questions at hand. The final step of WGS is data analysis. The analysis of whole-genome sequence is dependent on highly sophisticated bioinformatics tools that the research personal have to buy. However, many laboratories and research institutions do not have the bioinformatics capabilities to analyze the genomic data and therefore, are unable to take maximum advantage of whole-genome sequencing. In this aspect, this study provides a guide for research personals on a set of bioinformatics tools available online that can be used to analyze whole-genome sequence data of bacterial genomes. The web interfaces described here have many advantages and, in most cases exempting the need for costly analysis tools and intensive computing resources.
Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data
Lee, Sungyoung,Kwon, Min-Seok,Park, Taesung Korea Genome Organization 2012 Genomics & informatics Vol.10 No.4
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 ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ 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 $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ 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 $G{\times}G$ 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 $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.
HisCoM-GGI: Software for Hierarchical Structural Component Analysis of Gene-Gene Interactions
Choi, Sungkyoung,Lee, Sungyoung,Park, Taesung Korea Genome Organization 2018 Genomics & informatics Vol.16 No.4
Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.
ENCODE: A Sourcebook of Epigenomes and Chromatin Language
Yavartanoo, Maryam,Choi, Jung Kyoon Korea Genome Organization 2013 Genomics & informatics Vol.11 No.1
Until recently, since the Human Genome Project, the general view has been that the majority of the human genome is composed of junk DNA and has little or no selective advantage to the organism. Now we know that this conclusion is an oversimplification. In April 2003, the National Human Genome Research Institute (NHGRI) launched an international research consortium called Encyclopedia of DNA Elements (ENCODE) to uncover non-coding functional elements in the human genome. The result of this project has identified a set of new DNA regulatory elements, based on novel relationships among chromatin accessibility, histone modifications, nucleosome positioning, DNA methylation, transcription, and the occupancy of sequence-specific factors. The project gives us new insights into the organization and regulation of the human genome and epigenome. Here, we sought to summarize particular aspects of the ENCODE project and highlight the features and data that have recently been released. At the end of this review, we have summarized a case study we conducted using the ENCODE epigenome data.
Comparison of the Affymetrix SNP Array 5.0 and Oligoarray Platforms for Defining CNV
Kim, Ji-Hong,Jung, Seung-Hyun,Hu, Hae-Jin,Yim, Seon-Hee,Chung, Yeun-Jun Korea Genome Organization 2010 Genomics & informatics Vol.8 No.3
Together with single nucleotide polymorphism (SNP), copy number variations (CNV) are recognized to be the major component of human genetic diversity and used as a genetic marker in many disease association studies. Affymetrix Genome-wide SNP 5.0 is one of the commonly used SNP array platforms for SNP-GWAS as well as CNV analysis. However, there has been no report that validated the accuracy and reproducibility of CNVs identified by Affymetrix SNP array 5.0. In this study, we compared the characteristics of CNVs from the same set of genomic DNAs detected by three different array platforms; Affymetrix SNP array 5.0, Agilent 2X244K CNV array and NimbleGen 2.1M CNV array. In our analysis, Affymetrix SNP array 5.0 seems to detect CNVs in a reliable manner, which can be applied for association studies. However, for the purpose of defining CNVs in detail, Affymetrix Genome-wide SNP 5.0 might be relatively less ideal than NimbleGen 2.1M CNV array and Agilent 2X244K CNV array, which outperform Affymetrix array for defining the small-sized single copy variants. This result will help researchers to select a suitable array platform for CNV analysis.
Lee, Tae-Rim,Ahn, Jin Mo,Kim, Gyuhee,Kim, Sangsoo Korea Genome Organization 2017 Genomics & informatics Vol.15 No.4
Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.
Melka, Hailu Dadi,Jeon, Eun-Kyeong,Kim, Sang-Wook,Han, James-Bond,Yoon, Du-Hak,Kim, Kwan-Suk Korea Genome Organization 2011 Genomics & informatics Vol.9 No.2
The use of genomic information in genomic selection programs for dairy and beef cattle breeds has become a reality in recent years. In this investigation, we analyzed single-nucleotide polymorphisms (SNPs) for Hanwoo (n=50) and Holstein (n=50) breeds using the Illumina Bovine SNP50 BeadChip to facilitate genomic selection and utilization of the Hanwoo breed in Korea. Analysis of the entire genomes showed different spectra of SNP frequencies for Hanwoo and Holstein cattle. The study revealed a highly significant (p<0.001) difference between Hanwoo and Holstein cattle in minor allele frequency (MAF). The average MAFs were $0.19{\pm}0.16$ and $0.22{\pm}0.16$ for Hanwoo and Holstein, respectively. From the total of 52,337 SNPs that were successfully identified, about 72% and 79% were polymorphic in Hanwoos and Holsteins, respectively. Polymorphic and fixed SNPs were not distributed uniformly across the chromosomes within breeds or between the two breeds. The number of fixed SNPs on all chromosomes was higher in Hanwoo cattle, reflecting the genetic uniqueness of the Hanwoo breed. In general, the rate of polymorphisms detected in these two breeds suggests that the SNPs can be used for different applications, such as whole-genome association and comparative genetic studies, and are a helpful tool in developing breed identification genetic markers.
Genome-wide Association Study Identified TIMP2 Genetic Variant with Susceptibility to Osteoarthritis
Keam, Bhum-Suk,Hwang, Joo-Yeon,Go, Min-Jin,Heo, Jee-Yeon,Park, Mi-Sun,Lee, Ji-Young,Kim, Nam-Hee,Park, Miey,Oh, Ji-Hee,Kim, Dong-Hyun,Jeong, Jin-Young,Lee, Jong-Young,Han, Bok-Ghee,Lee, Ju-Young Korea Genome Organization 2011 Genomics & informatics Vol.9 No.3
Osteoarthritis (OA) is the most common degenerative joint disorder in the elderly population. To identify OA-associated genetic variants and candidate genes, we conducted a genome-wide association study (GWAS). A total 3,793 samples (476 cases: wrist + knee and 3317 controls) from a community-based epidemiological study were genotyped using the Affymetrix SNP 5.0. An intronic SNP (rs4789934) in the TIMP2 (tissue inhibitor of metalloproteinase-2) showed the most significance with OA (odd ratio [OR] = 2.06, 95% confidence interval [CI] = 1.52-2.81, p = $4.01{\times}10^{-6}$). Furthermore, a poly-morphism (rs1352677) in the NKAIN2 ($Na^+/K^+$ transporting ATPase interacting 2) was suggestively associated with OA (OR = 1.43, CI = 1.22-1.66, p = $7.01{\times}10^{-6}$). The present study provides new insights into the identification of genetic predisposing factors for OA.
Combined Genome Mapping of RFLP-AFLP-SSR in Pepper
Lee, Je Min,Kim, Byung-Dong Korea Genome Organization 2003 Genomics & informatics Vol.1 No.2
We have constructed a molecular linkage map of pepper (Capsicum spp.) in an interspecific $F_2$ population of 107 plants with 320 RFLP, 136 AFLP, and 46 SSR markers. The resulting linkage map consists of 15 linkage groups covering 1,720 cM with an average map distance of 3.7 cM between framework markers. Most RFLP markers ($80\%$) were pepper-derived clones and these markers were evenly distributed all over the genome. Genes for defense and biosynthesis of carotenoids and capsaicinoids were mapped on this linkage map. By using 30 primer combinations, AFLP markers were generated in the $F_2$ population. For development of SSR markers in Capsicum, microsatellites were isolated from two small-insert genomic libraries and the GenBank database. This combined map provides a starting point for high-resolution QTL analysis, gene isolation, and molecular breeding.